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I ve had substantial experience with clusters set up with a single map task per TaskTracker, and have set the number of threads used by the MultithreadedMapRunner class to tune the task for full CPU utilization (roughly 80% to 90%). In one particular job, there was a large variance in the time it took to process a key: some keys took three hours, and others three seconds. If the long-running keys came late in an input split, the task would end up running one thread and idle six of the processors on the machine. The only solution for this was to reorder the input keys so that the long-running keys came first in the splits, or to abandon the longrunning keys after a set elapsed run time, and reprocess all of the long-running keys in an additional job later.

Here is one way to handle the password matching. Simply grab the values of the two password fields, and if they do not match, set a custom error. For the sake of a validation routine, it is probably acceptable just to set the error on one of the two password fields. If they do match, set the empty string as the custom error to clear it; this is the specified way for removing a custom error. Once you ve got the error set on the field, you can use the approaches described earlier in this chapter to show feedback to the user and let her change the passwords to match, as expected.

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ZooKeeper provides a framework for building high-performance collaborative services. ZooKeeper maintains a shared namespace that looks very similar to a hierarchical file system. Applications rendezvous on entries in the namespace. Each of these namespace entries may have data associated with it. The entry data is accessed atomically, and changes are ordered. In addition, ZooKeeper provides an ephemeral node, an entry that vanishes when the service holding the entry open disconnects. The ephemeral nodes are used to establish service masters and sets of backup servers. Ephemeral nodes are used to support redundant servers with hot failover. ZooKeeper has been designed to be very reliable and very fast in environments in which data is primarily read. The examples at http://hadoop.apache.org/zookeeper/docs/current/recipes.html provide ZooKeeper recipes for two-phase commit, leader election, barriers, queues, and locks.

Atomicity: A transaction should be all or nothing. If it fails to complete, the database will be left as if none of the operations had ever been performed known as a rollback. Consistency: A transaction should be incapable of breaking any rules defined for the database. For example, foreign keys must be obeyed. If for some reason this is impossible, the transaction will be rolled back. Isolation: The effects of the transaction will be completely invisible to all other transactions until it has completed successfully. This guarantees that the transaction will always see the data in a sensible state. For example, an update to a user s address should only contain a correct address (i.e., it will never have the house name for one location but the zip code for another); without this rule, a transaction could easily see when another transaction had updated the first part but not yet completed. Durability: The data should be retained intact. If the system fails for any reason, it should always be possible to retrieve the database up to the moment of the failure.

The Lucene project, http://lucene.apache.org/java/docs/, provides the standard open source package used for search engines. The Lucene core provides the ability to take in documents in a variety of formats and build inverted indexes out of the terms found in the documents. Lucene also provides a query engine that takes incoming queries, searches the indexes, and returns the documents that match. Hadoop Core provides a contrib package that manages indexes that are stored in HDFS: contrib/index/hadoop-<rel>-index.jar. The main class, org.apache.hadoop.contrib.index. main.UpdateIndex, is specified in the JAR. The contrib package supports distributed indexes, shards, and unified indexes.

Summary

The SOLR project, http://lucene.apache.org/solr/, is a stand-alone, enterprise-grade search service built on top of Lucene. SOLR provides XML/HTTP and JSON APIs.

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