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CHAPTER 13 PARTITIONING
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that is less than 01-JAN-2005. However, the next insert of midnight on 01-JAN-2005 goes into partition PART_2 because that date/time is not strictly less than the partition range boundary. The last row obviously belongs in partition PART_2 since it is less than the partition range boundary for PART_2. We can confirm that this is the case by performing SELECT statements from the individual partitions: ops$tkyte@ORA10G> select to_char(range_key_column,'dd-mon-yyyy hh24:mi:ss') 2 from range_example partition (part_1); TO_CHAR(RANGE_KEY_CO -------------------15-dec-2004 00:00:00 31-dec-2004 23:59:59 ops$tkyte@ORA10G> select to_char(range_key_column,'dd-mon-yyyy hh24:mi:ss') 2 from range_example partition (part_2); TO_CHAR(RANGE_KEY_CO -------------------01-jan-2005 00:00:00 15-dec-2005 00:00:00 You might be wondering what would happen if you inserted a date that fell outside of the upper bound. The answer is that Oracle will raise an error: ops$tkyte@ORA10GR1> insert into range_example 2 ( range_key_column, data ) 3 values 4 ( to_date( '15/12/2007 00:00:00', 5 'dd/mm/yyyy hh24:mi:ss' ), 6 'application data...' ); insert into range_example * ERROR at line 1: ORA-14400: inserted partition key does not map to any partition Suppose you want to segregate 2005 and 2006 dates into their separate partitions as we have, but you want all other dates to go into a third partition. With range partitioning, you can do this using the MAXVALUE clause, which looks like this: ops$tkyte@ORA10GR1> CREATE TABLE range_example 2 ( range_key_column date , 3 data varchar2(20) 4 ) 5 PARTITION BY RANGE (range_key_column) 6 ( PARTITION part_1 VALUES LESS THAN 7 (to_date('01/01/2005','dd/mm/yyyy')), 8 PARTITION part_2 VALUES LESS THAN
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CHAPTER 13 PARTITIONING
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(to_date('01/01/2006','dd/mm/yyyy')) PARTITION part_3 VALUES LESS THAN (MAXVALUE) ) /
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Table created. Now when you insert a row into that table, it will go into one of the three partitions no row will be rejected, since partition PART_3 can take any value of RANGE_KEY_COLUMN that doesn t go into PART_1 or PART_2 (even null values of the RANGE_KEY_COLUMN will be inserted into this new partition).
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Hash Partitioning
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When hash partitioning a table, Oracle will apply a hash function to the partition key to determine in which of the N partitions the data should be placed. Oracle recommends that N be a number that is a power of 2 (2, 4, 8, 16, and so on) to achieve the best overall distribution, and we ll see shortly that this is absolutely good advice.
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How Hash Partitioning Works
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Hash partitioning is designed to achieve a good spread of data across many different devices (disks), or just to segregate data out into more manageable chunks. The hash key chosen for a table should be a column or set of columns that are unique, or at least have as many distinct values as possible to provide for a good spread of the rows across partitions. If you choose a column that has only four values, and you use two partitions, then all the rows could quite easily end up hashing to the same partition, obviating the goal of partitioning in the first place! We will create a hash table with two partitions in this case. We will use a column named HASH_KEY_COLUMN as our partition key. Oracle will take the value in this column and determine the partition this row will be stored in by hashing that value: ops$tkyte@ORA10G> CREATE TABLE hash_example 2 ( hash_key_column date, 3 data varchar2(20) 4 ) 5 PARTITION BY HASH (hash_key_column) 6 ( partition part_1 tablespace p1, 7 partition part_2 tablespace p2 8 ) 9 / Table created. Figure 13-2 shows that Oracle will inspect the value in the HASH_KEY_COLUMN, hash it, and determine which of the two partitions a given row will appear in:
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CHAPTER 13 PARTITIONING
PART_1
PART_2
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). 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. 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.
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