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CHAPTER 10 DATABASE TABLES
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Now that you know how to see most of the options available to you on a given CREATE TABLE statement, which are the important ones you need to be aware of for heap tables In my opinion, there are two with ASSM and four with MSSM: FREELISTS: MSSM only. Every table manages the blocks it has allocated in the heap on a freelist. A table may have more than one freelist. If you anticipate heavy insertion into a table by many concurrent users, configuring more than one freelist can have a major positive impact on performance (at the cost of possible additional storage). Refer to the previous discussion and example in the section FREELISTS for the sort of impact this setting can have on performance. PCTFREE: Both ASSM and MSSM. A measure of how full a block can be is made during the INSERT process. As shown earlier, this is used to control whether a row may be added to a block or not based on how full the block currently is. This option is also used to control row migrations caused by subsequent updates and needs to be set based on how you use the table. PCTUSED: MSSM only. A measure of how empty a block must become before it can be a candidate for insertion again. A block that has less than PCTUSED space used is a candidate for insertion of new rows. Again, like PCTFREE, you must consider how you will be using your table to set this option appropriately. INITRANS: Both ASSM and MSSM. The number of transaction slots initially allocated to a block. If set too low (it defaults to and has a minimum of 2), this option can cause concurrency issues in a block that is accessed by many users. If a database block is nearly full and the transaction list cannot be dynamically expanded, sessions will queue up waiting for this block, as each concurrent transaction needs a transaction slot. If you believe you will have many concurrent updates to the same blocks, you should consider increasing this value
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Note LOB data that is stored out of line in the LOB segment does not make use of the PCTFREE/PCTUSED
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parameters set for the table. These LOB blocks are managed differently: they are always filled to capacity and returned to the freelist only when completely empty.
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These are the parameters you want to pay particularly close attention to. With the introduction of locally managed tablespaces, which are highly recommended, I find that the rest of the storage parameters (such as PCTINCREASE, NEXT, and so on) are simply not relevant anymore.
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Index organized tables (IOTs) are, quite simply, tables stored in an index structure. Whereas a table stored in a heap is unorganized (i.e., data goes wherever there is available space), data in an IOT is stored and sorted by primary key. IOTs behave just like regular tables do as far as your application is concerned; you use SQL to access them as normal. They are especially useful for information retrieval, spatial, and OLAP applications.
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CHAPTER 10 DATABASE TABLES
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What is the point of an IOT You might ask the converse, actually: what is the point of a heap organized table Since all tables in a relational database are supposed to have a primary key anyway, isn t a heap organized table just a waste of space We have to make room for both the table and the index on the primary key of the table when using a heap organized table. With an IOT, the space overhead of the primary key index is removed, as the index is the data and the data is the index. The fact is that an index is a complex data structure that requires a lot of work to manage and maintain, and the maintenance requirements increase as the width of the row to store increases. A heap, on the other hand, is trivial to manage by comparison. There are efficiencies in a heap organized table over an IOT. That said, IOTs have some definite advantages over their heap counterparts. For example, I remember once building an inverted list index on some textual data (this predated the introduction of interMedia and related technologies). I had a table full of documents, and I would parse the documents and find words within them. I had a table that then looked like this: create table keywords ( word varchar2(50), position int, doc_id int, primary key(word,position,doc_id) ); Here I had a table that consisted solely of columns of the primary key. I had over 100 percent overhead; the size of my table and primary key index were comparable (actually, the primary key index was larger since it physically stored the rowid of the row it pointed to, whereas a rowid is not stored in the table it is inferred). I only used this table with a WHERE clause on the WORD or WORD and POSITION columns. That is, I never used the table I used only the index on the table. The table itself was no more than overhead. I wanted to find all documents containing a given word (or near another word, and so on). The heap table was useless, and it just slowed down the application during maintenance of the KEYWORDS table and doubled the storage requirements. This is a perfect application for an IOT. Another implementation that begs for an IOT is a code lookup table. Here you might have ZIP_CODE to STATE lookup, for example. You can now do away with the heap table and just use an IOT itself. Anytime you have a table that you access via its primary key exclusively, it is a candidate for an IOT. When you want to enforce co-location of data or you want data to be physically stored in a specific order, the IOT is the structure for you. For users of Sybase and SQL Server, this is where you would have used a clustered index, but IOTs go one better. A clustered index in those databases may have up to a 110 percent overhead (similar to the previous KEYWORDS table example). Here, we have a 0 percent overhead since the data is stored only once. A classic example of when you might want this physically co-located data would be in a parent/child relationship. Let s say the EMP table had a child table containing addresses. You might have a home address entered into the system when the employee is initially sent an offer letter for a job, and later he adds his work address. Over time, he moves and changes the home address to a previous address and adds a new home address. Then he has a school address he added when he went back for a degree, and so on. That is, the employee has three or four (or more) detail records, but these details arrive randomly over time. In a normal heap-based table, they just go anywhere. The odds that two or more of the address records would be on the same database block in the heap table are very near zero. However, when you query an employee s
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