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CHAPTER 11 INDEXES
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Table created. ops$tkyte@ORA11GR2> create index t_idx on 2 t(owner,object_type,object_name); Index created. ops$tkyte@ORA11GR2> analyze index t_idx validate structure;; Index analyzed. We then create an IDX_STATS table in which to save INDEX_STATS information, and we label the rows in the table as noncompressed : ops$tkyte@ORA11GR2> create table idx_stats 2 as 3 select 'noncompressed' what, a.* 4 from index_stats a; Table created. Now, we could realize that the OWNER component is repeated many times, meaning that a single index block in this index will have dozens of entries, as shown in Figure 11-2.
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Figure 11-2. Index block with OWNER column repeated We could factor the repeated OWNER column out of this, resulting in a block that looks more like Figure 11-3.
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Figure 11-3. Index block with OWNER column factored out
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CHAPTER 11 INDEXES
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In Figure 11-3, the owner name appears once on the leaf block not once per repeated entry. We run the following script, passing in the number 1, to re-create the scenario whereby the index is using compression on just the leading column: drop index t_idx; create index t_idx on t(owner,object_type,object_name) compress &1; analyze index t_idx validate structure; insert into idx_stats select 'compress &1', a.* from index_stats a; For comparison reasons, we run this script not only with one column, but also two and three compressed columns, to see what happens. At the end, we query IDX_STATS and should observe this: ops$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
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Note The ANALYZE command against the noncompressed index populated the
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OPT_CMPR_PCTSAVE/OPT_CMPR_COUNT columns and estimated a 28 percent savings with COMPRESS 2, and we
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achieved just about exactly that.
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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
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CHAPTER 11 INDEXES
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.
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