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In Figure 11-3, the owner name appears once on the leaf block not once per repeated entry. We run the following script, passing in the number 1, to re-create the scenario whereby the index is using compression on just the leading column: drop index t_idx; create index t_idx on t(owner,object_type,object_name) compress &1; analyze index t_idx validate structure; insert into idx_stats select 'compress &1', a.* from index_stats a; For comparison reasons, we run this script not only with one column, but also two and three compressed columns, to see what happens. At the end, we query IDX_STATS and should observe this: ops$tkyte@ORA11GR2> select what, height, lf_blks, br_blks, 2 btree_space, opt_cmpr_count, opt_cmpr_pctsave 3 from idx_stats 4 / WHAT HEIGHT LF_BLKS BR_BLKS BTREE_SPACE OPT_CMPR_COUNT OPT_CMPR_PCTSAVE ------------- ------ ------- ------- ----------- -------------- ---------------noncompressed 3 351 3 2830680 2 28 compress 1 3 314 3 2533572 2 19 compress 2 2 253 1 2030004 2 0 compress 3 3 393 3 3164940 2 3535 We see that the COMPRESS 1 index is about 89 percent the size of the noncompressed index (comparing BTREE_SPACE). The number of leaf blocks has decreased measurably. Further, when we use COMPRESS 2, the savings are even more impressive. The resulting index is about 72 percent the size of the original, and so much data is able to be placed on individual blocks that the height of the index actually decreased from 3 to 2. In fact, using the column OPT_CMPR_PCTSAVE, which stands for optimum compression percent saved or the expected savings from compression, we could have guessed the size of the COMPRESS 2 index: ops$tkyte%ORA11GR2> select 2830680*(1-0.28) from dual; 2830680*(1-0.28) ---------------2038089.6

ot unlike the physical theories with their supporting time-dependent equations, communications between networking devices rely on time as well Throughout this and subsequent chapters, the classical physics view of time is assumed The reader is referred to 1 for additional perspectives on the concept of time, which permeates the operations of numerous networking protocols, not just NTP Various timers and time interval parameters (update, invalid, holddown, flush, hello, dead, wait, retransmit, keepalive, and more) exist within routing and other protocols such as ICMP RIP IRGP OSPF, and BGP Network administrators need to be familiar with the , , , actions of these time-related parameters and configure them judiciously Those parameters are almost without exception absolutely integral to the proper functioning of the protocols Inexperienced tinkering with protocol timers can lead to disasters on the order of bringing down entire networks.

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But notice what happens with COMPRESS 3. The resulting index is actually larger: 110 percent the size of the original index. This is due to the fact that each repeated prefix we remove saves the space of N

It is important to realize that the n-level undo capability implemented in the framework is optional and is designed to incur no overhead if it is not used.

copies, but adds 4 bytes of overhead on the leaf block as part of the compression scheme. By adding in the OBJECT_NAME column to the compressed key, we made that key almost unique in this case meaning there were really no duplicate copies to factor out. Therefore, we ended up adding 4 bytes to almost every single index key entry and factoring out no repeating data. The OPT_CMPR_COUNT column in IDX_STATS is dead accurate at providing the best compression count to be used, and OPT_CMPR_PCTSAVE will tell you exactly how much savings to expect. Now, you do not get this compression for free. The compressed index structure is now more complex than it used to be. Oracle will spend more time processing the data in this structure, both while maintaining the index during modifications and when you search the index during a query. What we are doing here is trading off increased CPU time for reduced I/O time. With compression, our block buffer cache will be able to hold more index entries than before, our cache-hit ratio might go up, and our physical I/Os should go down, but it will take a little more CPU horsepower to process the index, and it will also increase the chance of block contention. Just as in our discussion of the hash cluster, where it might take more CPU to retrieve a million random rows but half the I/O, we must be aware of the tradeoff. If you are currently CPU bound, adding compressed key indexes may slow down your processing. On the other hand, if you are I/O bound, using them may speed up things.

Another feature of a B*Tree index is the ability to reverse its keys. At first you might ask yourself, Why would I want to do that B*Tree indexes were designed for a specific environment and for a specific issue. They were implemented to reduce contention for index leaf blocks in right-hand-side indexes, such as indexes on columns populated by a sequence value or a timestamp, in an Oracle RAC environment.

Consider the case of an Invoice object that contains a collection of LineItem objects. The Invoice itself contains data that the user can edit plus data that s derived from the collection. The TotalAmount property of an Invoice, for instance, is calculated by summing up the individual Amount properties of its LineItem objects. Figure 2-2 illustrates this arrangement.

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