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This is done by adding a new property to these objects called $constructor Like its native constructor counterpart, the $constructor property is a reference to the object s original constructor function Type objects therefore have their $constructor properties pointing to Type, while classes have theirs pointing to Class Of course, just because MooTools added a new property that resembles the native constructor property doesn t mean that instanceof will respect it The MooTools type system works on a separate level from the native one, so we can t expect a simple solution like that to take care of the issue altogether Instead, MooTools adds the second part of the equation by creating a new function to replace the instanceof operator: the instanceOf function The instanceOf function takes in two arguments: item, which is the object being checked, and object, which should be a constructor function to check against.

nce you have developed your MapReduce job, you need to be able to run it at scale on your cluster. A number of factors influence how your job scales. This chapter will cover how to recognize that your job is having a problem and how to tune the scaling parameters so that your job performs optimally. First, we ll look at tunable items. The framework provides several parameters that let you tune how your job will run on the cluster. Most of these take effect at the job level, but a few work at the cluster level. With large clusters of machines, it becomes important to have a simple monitoring framework that provides a visual indication of how the cluster is and has been performing. Having alerts delivered when a problem is developing or occurs is also essential. This chapter introduces several tools for monitoring Hadoop services. Finally, you ll get some tips on what to do when your job isn t performing as it should. Your jobs may be failing or running slowly. This chapter is focused on tuning jobs running on the cluster, rather than debugging the jobs themselves. Debugging is covered in the next chapter.

With data connections, you start to enter a world of authentication complexity. Whether you re designing a rich client form that will post to a database or a browser form that posts to a web service, you need to be aware of how authentication works, and especially the double-hop problem. When a user posts to a web page, web service, or any kind of server, they are passing their credentials from the authenticated client to the server. Once at the server, the process that receives the credentials (in the case of a web service, this is IIS) can pass them to any other service on the same machine (SQL Server, for example). However, the service holding the credentials cannot, by default, forward those credentials to another server. This is referred to as the double-hop problem there s one hop from the user console to the web server, and another hop from the web server to the database server (see Figure 7-1).

Hadoop Core is designed for running jobs that have large input data sets and medium to large outputs, running on large sets of dissimilar machines. The framework has been heavily optimized for this use case. Hadoop Core is optimized for clusters of heterogeneous machines that are not highly reliable. The HDFS file system is optimized for small numbers of very large files that are accessed sequentially. The optimal job is one that uses as input a dataset composed of a number of large input files, where each input file is at least 64MB in size and transforms this data via a MapReduce job into a small number of large files, again where each file is at least 64MB. The data stored in HDFS is generally not considered valuable or irreplaceable. The service level agreement (SLA) for jobs is long and can sustain recovery from machine failure. Users commonly get into trouble when their jobs input large numbers of small files, output large numbers of small files, or require random access to files. Another problem is a need for rapid access to data or for rapid turnover of jobs. HDFS installations get into trouble when large numbers of files are being created or exist on the DataNodes.