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Figure 13-2 Hash partition insert example As noted earlier, hash partitioning gives you no control over which partition a row ends up in Oracle applies the hash function and the outcome of that hash determines where the row goes If you want a specific row to go into partition PART_1 for whatever reason, you should not in fact, you cannot use hash partitioning The row will go into whatever partition the hash function says to put it in If you change the number of hash partitions, the data will be redistributed over all of the partitions (adding or removing a partition to a hash partitioned table will cause all of the data to be rewritten, as every row may now belong in a different partition).

Most OLTP applications are not able to take advantage of the times where partitioning is able to enhance query performance, but you can still benefit from the other two possible partitioning benefits, administrative ease and higher availability..

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Hash partitioning is most useful when you have a large table, such as the one shown in the Reduced Administrative Burden section, and you would like to divide and conquer it Rather than manage one large table, you would like to have 8 or 16 smaller tables to manage Hash partitioning is also useful to increase availability to some degree, as demonstrated in the Increased Availability section; the temporary loss of a single hash partition permits access to all of the remaining partitions Some users may be affected, but there is a good chance that many will not be Additionally, the unit of recovery is much smaller now You do not have a single large table to restore and recover; you have a fraction of that table to recover.

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In a data warehouse/decision-support system, partitioning is not only a great administrative tool, but something that will speed up processing. For example, you may have a large table on which you need to perform an ad hoc query. You always do the ad hoc query by sales quarter, as each sales quarter contains hundreds of thousands of records and you have millions of online records. So, you want to query a relatively small slice of the entire data set, but it is not really feasible to index it based on the sales quarter. This index would point to hundreds of thousands of records, and doing the index range scan in this way would be terrible (refer to 11 Indexes for more details on this). A full table scan is called for to process many of your queries, but you end up having to scan millions of records, most of which won t apply to your query. Using an intelligent partitioning scheme, you can segregate the data by quarter such that when you query the data for any given quarter, you will full scan only that quarter s data. This is the best of all possible solutions. In addition, in a data warehouse/decision-support system environment, parallel query is used frequently. Here, operations such as parallel index range scans or parallel fast full index scans are not only meaningful, but also beneficial to us. We want to maximize our use of all available resources, and parallel query is a way to do it. So, in this environment, partitioning stands a very good chance of speeding up processing.

Lastly, hash partitioning is useful in high update contention environments, as mentioned in the Enhanced Statement Performance section when we talked about OLTP systems Instead of having a single hot segment, we can hash partition a segment into 16 pieces, each of which is now receiving modifications..

There are currently six methods by which you can partition tables in Oracle: Range partitioning: You may specify ranges of data that should be stored together For example, everything that has a timestamp within the month of Jan-2010 will be stored in partition 1, everything with a timestamp within Feb-2010 in partition 2, and so on This is probably the most commonly used partitioning mechanism in Oracle Hash partitioning: You saw this in the first example in this chapter A column (or columns) has a hash function applied to it, and the row will be placed into a partition according to the value of this hash List partitioning: You specify a discrete set of values, which determines the data that should be stored together.

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