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The CPU is maxed out, the compression level is too high, or the compression algorithm is computationally too expensive Saturation of the outbound network connection with traffic for HDFS If it is not saturated, more writes could be delivered to this DataNode

In 7, we learned about trees, which are special data structures composed of several linked nodes. We learned that all trees have one node from which all other nodes come, and we called that the root node. We also learned that nodes have parents, children, and even siblings like one big, happy family. What we didn t discuss, however, is how we find a node in this family. Let s look again at a simple tree structure (Figure 9 1).

Increase the number of transfer threads or provide a higher-speed network connection. Increase the number of simultaneous threads writing or reduce the number of files being created by increasing the individual file sizes. Add more independent locations to dfs.data.dir or add more DataNodes.

The next design task pane is the controls pane. Click the Controls link to show the Controls pane (Figure 3-12). What you see may depend on whether you re working on a browser form. The controls are broken down into five groups: Standard, Repeating and Optional, File and Picture, Advanced, and Custom.

If you have a large number of files that need to be stored in HDFS prior to the task start, such as might occur if your job needs to populate the job input directory, there are several things you may try, in varying combinations: It may be faster to copy the files from a machine that hosts a DataNode, as all of the writes will first go to the local DataNode, and the application will not have to wait for the data to traverse the network The downside is that one replica of every block will end up on the local DataNode, greatly reducing the opportunity for data to be local to a map task The DataNode may also get unbalanced with respect to storage, compared to other DataNodes.

Figure 9 1. A simple tree with six nodes This is the tree we first saw in 7. It has 6 nodes: A, B1, B2, C1, C2, and D. Now let s say we are tasked to find the node marked as C1. How do we find it

Ideally, bulk input of data to be used as input to a map task should be input from a host that does not also provide DataNode services, to ensure even distribution of the stored blocks across the DataNodes It may be faster to run the copies in parallel The limiting factor will be the network speed or the local DataNode disk speed in the event the copy host is also a DataNode Use compression for data to be used once LZO provides very good compression at little CPU overhead, provided that a native implementation is available Create an archive of the input files, so that fewer files need to be created in HDFS The downside is that zip and tar archives must be processed whole by a map task and may not be split into pieces (at least through Hadoop 0190).

Of course, this might sound silly You can clearly see where C1 is in the diagram and you can just point at it with your finger, so this isn t much of a challenge But what if there was no diagram and all we had was code: var tree = { root: { name: 'A', children: [ { name: 'B1', children: [] }, { name: 'B2', children: [ { name: 'C1', children: [ { name: 'D', children: [] } ] }, { name: 'C2', children: [] } ] } ] } }; This snippet transforms our tree from a diagram into a JavaScript object We kept everything fairly simple: each object aside from the tree object itself is a node Nodes have two properties: name and children The name property is a string and it s the representation of the node s actual name.