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range of data in a table column, and if the MapFile grows past a specified size, it is split into multiple MapFiles More recent versions of HBase also provide a memcached-based intermediate layer between the user and the MapFiles (http://wwwdangacom/memcached/) Prior to the addition of the memcached layer, HBase suffered terrible performance for random reads and writes, primarily because HDFS is not optimized for low latency random access Ordered reads and writes perform at near-HDFS speed HBase has a number of server processes, a single HBaseMaster that manages the HBase cluster and a set of HRegionServers, each of which is responsible for a set of MapFiles containing column regions.

We will not try to discuss each of the methods available to you through the Session interface, but we will look at some of the method categories that Session provides (see Table 9-1). For an exhaustive look at what is available, you should read the API documentation on the Hibernate website, or in the Hibernate 3 download.

Sometimes there are techniques that don t fit into our regular examples, but which nonetheless apply to many types of HTML5 applications. We present to you a short, but common, practical extra here.

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HBase suffers terribly from the inability of applications to flush file data to storage before the file is closed, and a crash of any portion of the HBase servers or a service interrupting crash of HDFS will result in data loss In prior chapters there was a discussion of problems caused by applications or server processes attempting to exceed the system-imposed limit on the number of open files; HBase also has this problem The problem is substantially aggravated because each Hadoop MapFile is actually two files and a directory in HDFS, and each HDFS file also has a hidden checksum file Setting the per-process open file count very large is a necessity for the HBase servers A storage file format, HFile, is under development and due for Hbase version 0200, and is expected to solve many of the performance and reliability issues.

HBase relies utterly on a smoothly performing HDFS for its operation; any stalls or DataNode instability will show up as HBase errors There are HDFS tuning parameters suggested in the troubleshooting section on the HBase wiki: http://wikiapacheorg/hadoop/ Hbase/Troubleshooting In particular, if the underlying HDFS cluster is experiencing a slow block report problem, https://issuesapacheorg/jira/browse/HADOOP-4584, HBase is not recommended HBase servers, particularly the version using memcached, are memory intensive and generally require at least a gigabyte of real memory per server; any paging will drastically affect performance Java Virtual Machine (JVM) garbage collection thread stalls are also causing HBase failures HBase generally provides downloadable release bundles that track the Hadoop Core distributions HBase is not part of the Hadoop Core distribution..

The Password is: Validation!

save saveOrUpdate persist get getIdentifier load refresh update delete createFilter createQuery beginTransaction lock

Hive provides a rich set of tools in multiple languages to perform SQL-like data analysis on data stored in HDFS. The wonderful people at Facebook have contributed Hive to the Apache project. As of the publication of this book, Hive is undergoing active development. Compiled versions of Hive are part of the contrib subtree of the Hadoop Core distribution. Cloudera, discussed later in this chapter, provides online training for Hive.

The following four lines are required before attempting to start Hive (your installation might already have the /tmp and /user/hive/warehouse directories present):

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