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Here, we will look at tuning the cluster system and the HDFS parameters for performance and reliability. Commonly, the two most important factors are network bandwidth and disk throughput. Memory use and CPU overhead for thread handling may also be issues. HDFS uses a basic file system block size of 64MB, and the JobTracker also chunks task input into 64MB segments. Using large blocks helps reduce the cost of a disk seek compared with the read/write time, thereby increasing the aggregate I/O rate when multiple requests are active. The large input-split size reduces the ratio of task setup time to task run time, as there is work to be done to set up a task before the TaskTracker can start the mapper or reducer on the input split. The various tuning factors available control the maximum number of requests in progress. In general, the more requests in progress, the more contention there is for storage operations and network bandwidth, with a corresponding increase in memory requirements and CPU overhead for handling all of the outstanding requests. If the number of requests allowed is too low, the cluster may not fully utilize the disk or network bandwidth, or cause requests to timeout. Most of the tuning parameters for HDFS do not yet have exact science behind the settings. Currently, tuning is ad hoc and per cluster. In general, the selections are a compromise between various factors. Finding the sweet spot requires knowledge of the local system and experience. Let s examine each of the tuning factors in turn.

Hibernate provides a mechanism to refresh persistent objects from their database representations. Typically, you will not have to use this, but it could come into use for one of three scenarios. The first scenario is that your Hibernate application is not the only application working with this data. Rather than using locks to control access, you only need to load the properties into the object for reading at this time. Another scenario is that your application executed some SQL directly against the database, and now the data in memory is out of sync with the database representation. The last scenario is that your database uses triggers to populate properties on the object for instance, some database designs include a created timestamp as a column that gets populated by a trigger when the record is first stored in the database. Use one of the refresh() methods on the Session interface to refresh an instance of a persistent object: public void refresh(Object object) throws HibernateException public void refresh(Object object, LockMode lockMode) throws HibernateException Although you may be used to the idea of refreshing objects from using other Java persistence systems or tools, we have found that in Hibernate, you typically do not have to worry about using these refresh methods.

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Hadoop Core uses large numbers of file descriptors for MapReduce, and the DFSClient uses a large number of file descriptors for communicating with the HDFS NameNode and DataNode server processes. The DFSClient code also presents a misleading error message when there has been a failure to allocate a file descriptor: No live nodes contain current block.

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note The class org.apache.hadoop.hdfs.DFSClient provides the actual file services for applications

interacting with the HDFS file system. All file system operations and file operations performed by the application will be translated into method calls on the DFSClient object, which will in turn issue the appropriate Remote Procedure Call (RPC) calls to the NameNode and the DataNodes relevant for the operations. As of Hadoop 0.18, these RPC calls use the Java NIO services. Prior to Hadoop 0.18, blocking operations and fixed timeouts were used for the RPC calls.

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