By Tom White
Prepare to unencumber the facility of your info. With the fourth variation of this entire consultant, you’ll the right way to construct and preserve trustworthy, scalable, allotted structures with Apache Hadoop. This e-book is perfect for programmers seeking to study datasets of any measurement, and for directors who are looking to organize and run Hadoop clusters.
Using Hadoop 2 completely, writer Tom White provides new chapters on YARN and a number of other Hadoop-related tasks comparable to Parquet, Flume, Crunch, and Spark. You’ll know about contemporary adjustments to Hadoop, and discover new case reports on Hadoop’s position in healthcare platforms and genomics info processing.
• study primary elements reminiscent of MapReduce, HDFS, and YARN
• discover MapReduce intensive, together with steps for constructing functions with it
• organize and hold a Hadoop cluster working HDFS and MapReduce on YARN
• study facts codecs: Avro for info serialization and Parquet for nested data
• Use facts ingestion instruments similar to Flume (for streaming information) and Sqoop (for bulk facts transfer)
• know how high-level information processing instruments like Pig, Hive, Crunch, and Spark paintings with Hadoop
• research the HBase disbursed database and the ZooKeeper dispensed configuration provider
Read or Download Hadoop: The Definitive Guide (4th Edition) PDF
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Extra resources for Hadoop: The Definitive Guide (4th Edition)
In a nutshell, this is what Hadoop provides: a reliable, scalable platform for storage and analysis. What’s more, because it runs on commodity hardware and is open source, Hadoop is affordable. Querying All Your Data The approach taken by MapReduce may seem like a brute-force approach. The premise is that the entire dataset—or at least a good portion of it—can be processed for each query. But this is its power. MapReduce is a batch query processor, and the ability to run an ad hoc query against your whole dataset and get the results in a reasonable time is transformative.
7°C for 1901 (there were very few readings at the Analyzing the Data with Unix Tools | 21 beginning of the century, so this is plausible). The complete run for the century took 42 minutes in one run on a single EC2 High-CPU Extra Large instance. To speed up the processing, we need to run parts of the program in parallel. In theory, this is straightforward: we could process different years in different processes, using all the available hardware threads on a machine. There are a few problems with this, however.
The output from running the job provides some useful information. For example, we can see that the job was given an ID of job_local26392882_0001, and it ran one map task and one reduce task (with the following IDs: attempt_lo cal26392882_0001_m_000000_0 and attempt_local26392882_0001_r_000000_0). Knowing the job and task IDs can be very useful when debugging MapReduce jobs. The last section of the output, titled “Counters,” shows the statistics that Hadoop gen‐ erates for each job it runs. These are very useful for checking whether the amount of data processed is what you expected.