Category Archives: MapReduce

Upcoming Functionality in “Fair Scheduler 2.0”

Categories: General Hadoop MapReduce

(guest blog post by Matei Zaharia)

As Hadoop clusters grow in size and data volume, it becomes more and more useful to share them between multiple users and to isolate these users. If User 1 is running a ten-hour machine learning job for example, this should not impair a User 2 from running a 2-minute Hive query. In November, I blogged about how Hadoop 0.19 supports pluggable job schedulers,

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Configuration Parameters: What can you just ignore?

Categories: General Hadoop HDFS MapReduce

Configuring a Hadoop cluster is something akin to voodoo. There are a large number of variables in hadoop-default.xml that you can override in hadoop-site.xml. Some specify file paths on your system, but others adjust levers and knobs deep inside Hadoop’s guts. Unfortuately, there’s little or no documentation on how to set them well. Is there a single optimal configuration? Are there some settings that can just be “set to 11?”

Nigel's guitar goes to 11, but your cluster might not. At Cloudera,

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Database Access with Apache Hadoop

Categories: General Hadoop MapReduce

Editor’s note (added Nov. 9. 2013): Valuable data in an organization is often stored in relational database systems. To access that data, you could use external APIs as detailed in this blog post below, or you could use Apache Sqoop, an open source tool (packaged inside CDH) that allows users to import data from a relational database into Apache Hadoop for further processing. Sqoop can also export those results back to the database for consumption by other clients.

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Job Scheduling in Apache Hadoop

Categories: Hadoop MapReduce

(guest blog post by Matei Zaharia)

When Apache Hadoop started out, it was designed mainly for running large batch jobs such as web indexing and log mining. Users submitted jobs to a queue, and the cluster ran them in order. However, as organizations placed more data in their Hadoop clusters and developed more computations they wanted to run, another use case became attractive: sharing a MapReduce cluster between multiple users.

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Sending Files to Remote Task Nodes with Hadoop MapReduce

Categories: Hadoop MapReduce

It is common for a MapReduce program to require one or more files to be read by each map or reduce task before execution. For example, you may have a lookup table that needs to be parsed before processing a set of records. To address this scenario, Hadoop’s MapReduce implementation includes a distributed file cache that will manage copying your file(s) out to the task execution nodes.

The DistributedCache was introduced in Hadoop 0.7.0;

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