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(You ll deal more with reorg in 24) Let s explore the compression aspects of its use When you reorganize a table with the compression attribute set, the reorg utility implicitly follows the compression algorithms for Data Row Compression There is no need to explicitly instruct it to perform compression There is one option available that affects the behavior of compression, however, and it s concerned with whether an existing compression dictionary is preserved during reorganization or whether a new dictionary is generated Typically, you need to balance the time it takes to generate a dictionary with whether your data has substantially changed and would benefit from a fresh dictionary The two options you have when performing the reorganization are keepdictionary and resetdictionary The reorg command s syntax is prodigious, and I won t distract you with a multipage syntax diagram here.

The specific invocation for these compression options can be as simple as this: db2 => reorg table employee resetdictionary DB20000I The REORG command completed successfully..

@Begin(nested=true, pageflow="newuser") public void start() { // ... do work here ... }

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One last word on Data Row Compression is in order. As you might have experienced in other technical areas when using compression, it is very good for shrinking some types of data and not particularly good at other types. Text data types (and to a lesser extent numeric information) typically compresses very well using the Lempel Ziv algorithm, and you should see regular benefits from using Data Row Compression on this kind of data in DB2. Binary data such as BLOBs tends to have fewer benefits, particularly when you re storing image or video format data that has been created using schemes that embed compression. Like most compression technologies, you gain no advantage trying to compress alreadycompressed data. To test this, try compressing a .gif image file. The .gif format has Lempel Ziv compression as part of its makeup, so you ll see no benefit. IBM recognizes that any effort to do this leads to disappointment, so it has declared that Data Row Compression is simply not applicable for indexes, long objects, LOBs, and XML. You also can t mix Data Row Compression with replication. Finally, you might have noticed one other minor limitation with Data Row Compression: there s currently no GUI to manage compression, so you have to be comfortable with the DB2 CLP to manage compression for now.

Figure 3-2. The different options for resizing an image in GIMP Gimp offers the following resizing methods: None, Linear, Cubic, Sinc (Lanczos). None is the one described as Nearest Neighbor, and the rest are self explanatory. As explained before, for shrinking photographs in general, it is recommended to use the Sinc method and for enlarging photos, the Cubic method. Note that this is not a strict rule and you can experiment with both to see which one gives you better results for any particular photograph. It is important to note that there is also an option for resizing the Canvas, which is a different thing. You can access that option in Gimp by going into Image Canvas Size. The Canvas basically is the image container. Most of the time, you will want the Canvas to be the same size of your image, although if you want to make a collage for example, you will need to resize your Canvas first to the size of the collage and then start adding the other photos. As you can see in Figure 3-3, I decided to leave space for putting two images side by side. Because I wanted to change the aspect ratio of the Canvas, I had to click on the chain icon so that it allows me to change its width and height independently.

Here the start method will start a long-running conversation and start the page flow labeled newuser.

Summary

Summary

I tried to give you a quick flavor of some of the more advanced data-management features that DB2 has to offer. Each of the sections in this chapter should be treated as an entree to a larger topic that deserves much more attention, but you now have a working knowledge of replication, data compression, and different data and metadata movement techniques. You can now start thinking about how these tools might assist in your next DB2 project.

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