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Through at least Hadoop 0.19.0, the DataNode has two periodic tasks that do a linear scan of all of the data blocks stored by the DataNode. If this process starts taking longer than a small number of minutes, the DataNode will be marked as disconnected by the NameNode. When a DataNode is marked as disconnected, the NameNode queues all of the blocks that had a replica on that DataNode for replication. If the number of blocks is large, the NameNode may pause for a noticeable period while queuing the blocks. The only solutions for this at present are to add enough DataNodes, so that no DataNode has more than a few hundred thousand data blocks, or to alter your application s I/O patterns to use Hadoop archives or zip files to pack many small HDFS subblock-size files into single HDFS files. The latter approach results in a reduction in the number of blocks stored in HDFS and the number of blocks per DataNode. A simple way to work out how many blocks is too many is to run the following on a DataNode: time find dfs.data.dir ls > /dev/null If it takes longer than a few hundred seconds, you are in the danger zone. If it takes longer than a few minutes, you are in the pain zone. Replace dfs.data.dir with an expanded value from your Hadoop configuration. The timeout interval is 600 seconds, and hard-coded. If your ls takes anywhere close to that, your cluster will be unstable.

By default, this allows you to use the unqualified class names in Hibernate queries. You would only normally set this to false if the class name would otherwise be ambiguous. The database catalog against which queries should apply.

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Location data is sensitive information, so when you receive it, you must be careful about handling, storing, and retransmitting the data. Unless users give permission to store data, you should always dispose of the data after the task for which it was required is complete. Therefore, if you retransmit the location data, it is recommended that you first encrypt the data. Regarding the collection of geolocation data, your application should prominently show the following: That you are collecting location data Why you are collecting location data How long the location data is kept How you are securing the data How the location data is shared (if it is) How users can check and update their location data

HDFS is a wonderful global file system for a medium number of very large files. With reasonable care and an understanding of HDFS s limitations it will serve you well. HDFS is not a general-purpose file system to be used for large numbers of small files or for rapid creation and deletion of files. Through HDFS 0.19.0, the HDFS NameNode is a single point of failure and needs careful handling to minimize the risk of data loss. Using a separate machine for your secondary NameNode, and having multiple devices for the file system image and edit logs, will go a long way toward providing a fail-safe, rapid recovery solution.

Monitoring your DataNode s block totals will go a long way toward avoiding congestion collapse issues in your HDFS. You can do this by simply running a find on the dfs.data.dir and making sure that it takes less than a couple of minutes. Ensuring that your HDFS data traffic is segregated from your normal application traffic and crosses as few interswitch backhauls as possible will help to avoid network congestion and application misbehavior. Ultimately, remember that HDFS is designed for a small to medium number of very large files, and not for transitory storage of large numbers of small files.

In this section, we ll explore the use of the HTML5 Geolocation API in more detail. For the sake of illustration, we ve created a simple browser page geolocation.html. Remember that you can download all the code from the book's page on apress.com.

The default access type. If set to property, then get and set methods are used to access the data. If set to field, then the data is accessed directly. Alternatively, you can provide the class name of a PropertyAccessor implementation defining any other access mechanism.

rganizations run Hadoop Core to provide MapReduce services for their processing needs. They may have datasets that can t fit on a single machine, have time constraints that are impossible to satisfy with a small number of machines, or need to rapidly scale the computing power applied to a problem due to varying input set sizes. You will have your own unique reasons for running MapReduce applications. To do your job effectively, you need to understand all of the moving parts of a MapReduce cluster and of the Hadoop Core MapReduce framework. This chapter will raise the hood and show you some schematics of the engine. This chapter will also provide examples that you can use as the basis for your own MapReduce applications.

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