18 Partitioned Tables and Indexes

Partition Key

Each row in a partitioned table is unambiguously assigned to a single partition. The partition key is a set of one or more columns that determines the partition for each row. Oracle automatically directs insert, update, and delete operations to the appropriate partition through the use of the partition key. A partition key:

• Consists of an ordered list of 1 to 16 columns

• Cannot contain a LEVEL, ROWID, or MLSLABEL pseudocolumn or a column of type ROWID

• Can contain columns that are NULLable

Partitioned Tables

Tables can be partitioned into up to 1024K-1 separate partitions. Any table can be partitioned except those tables containing columns with LONG or LONG RAW datatypes. You can, however, use tables containing columns with CLOB or BLOB datatypes.

Partitioned Index-Organized Tables

You can partition index-organized tables by range, list, or hash. Partitioned index-organized tables are very useful for providing improved manageability, availability, and performance for index-organized tables. In addition, data cartridges that use index-organized tables can take advantage of the ability to partition their stored data. Common examples of this are the Image and interMedia cartridges.

For partitioning an index-organized table:

• Partition columns must be a subset of primary key columns

• Secondary indexes can be partitioned — locally and globally

• OVERFLOW data segments are always equipartitioned with the table partitions

Range Partitioning

Range partitioning maps data to partitions based on ranges of partition key values that you establish for each partition. It is the most common type of partitioning and is often used with dates. For example, you might want to partition sales data into monthly partitions.

When using range partitioning, consider the following rules:

• Each partition has a VALUES LESS THAN clause, which specifies a noninclusive upper bound for the partitions. Any values of the partition key equal to or higher than this literal are added to the next higher partition.

• All partitions, except the first, have an implicit lower bound specified by the VALUES LESS THAN clause on the previous partition.

• A MAXVALUE literal can be defined for the highest partition. MAXVALUE represents a virtual infinite value that sorts higher than any other possible value for the partition key, including the null value.

A typical example is given in the following section. The statement creates a table (sales_range) that is range partitioned on the sales_date field.

CREATE TABLE sales_range 
(salesman_id  NUMBER(5), 
salesman_name VARCHAR2(30), 
sales_amount  NUMBER(10), 
sales_date    DATE)
PARTITION BY RANGE(sales_date) 
(
PARTITION sales_jan2000 VALUES LESS THAN(TO_DATE('02/01/2000','MM/DD/YYYY')),
PARTITION sales_feb2000 VALUES LESS THAN(TO_DATE('03/01/2000','MM/DD/YYYY')),
PARTITION sales_mar2000 VALUES LESS THAN(TO_DATE('04/01/2000','MM/DD/YYYY')),
PARTITION sales_apr2000 VALUES LESS THAN(TO_DATE('05/01/2000','MM/DD/YYYY'))
);

List Partitioning

List partitioning enables you to explicitly control how rows map to partitions. You do this by specifying a list of discrete values for the partitioning key in the description for each partition. This is different from range partitioning, where a range of values is associated with a partition and from hash partitioning, where a hash function controls the row-to-partition mapping. The advantage of list partitioning is that you can group and organize unordered and unrelated sets of data in a natural way.

The details of list partitioning can best be described with an example. In this case, let's say you want to partition a sales table by region. That means grouping states together according to their geographical location as in the following example.

CREATE TABLE sales_list
(salesman_id  NUMBER(5), 
salesman_name VARCHAR2(30),
sales_state   VARCHAR2(20),
sales_amount  NUMBER(10), 
sales_date    DATE)
PARTITION BY LIST(sales_state)
(
PARTITION sales_west VALUES('California', 'Hawaii'),
PARTITION sales_east VALUES ('New York', 'Virginia', 'Florida'),
PARTITION sales_central VALUES('Texas', 'Illinois'),
PARTITION sales_other VALUES(DEFAULT)
);

Hash Partitioning

Hash partitioning enables easy partitioning of data that does not lend itself to range or list partitioning. It does this with a simple syntax and is easy to implement. It is a better choice than range partitioning when:

• You do not know beforehand how much data maps into a given range

• The sizes of range partitions would differ quite substantially or would be difficult to balance manually

• Range partitioning would cause the data to be undesirably clustered

• Performance features such as parallel DML, partition pruning, and partition-wise joins are important

The concepts of splitting, dropping or merging partitions do not apply to hash partitions. Instead, hash partitions can be added and coalesced.

CREATE TABLE sales_hash
(salesman_id  NUMBER(5), 
salesman_name VARCHAR2(30), 
sales_amount  NUMBER(10), 
week_no       NUMBER(2)) 
PARTITION BY HASH(salesman_id) 
PARTITIONS 4 
STORE IN (ts1, ts2, ts3, ts4);

Composite Partitioning

Composite partitioning partitions data using the range method, and within each partition, subpartitions it using the hash or list method. Composite range-hash partitioning provides the improved manageability of range partitioning and the data placement, striping, and parallelism advantages of hash partitioning. Composite range-list partitioning provides the manageability of range partitioning and the explicit control of list partitioning for the subpartitions.

Composite partitioning supports historical operations, such as adding new range partitions, but also provides higher degrees of parallelism for DML operations and finer granularity of data placement through subpartitioning.

Composite Partitioning Range-Hash Example

CREATE TABLE sales_composite 
(salesman_id  NUMBER(5), 
 salesman_name VARCHAR2(30), 
 sales_amount  NUMBER(10), 
 sales_date    DATE)
PARTITION BY RANGE(sales_date) 
SUBPARTITION BY HASH(salesman_id)
SUBPARTITION TEMPLATE(
SUBPARTITION sp1 TABLESPACE ts1,
SUBPARTITION sp2 TABLESPACE ts2,
SUBPARTITION sp3 TABLESPACE ts3,
SUBPARTITION sp4 TABLESPACE ts4)
(PARTITION sales_jan2000 VALUES LESS THAN(TO_DATE('02/01/2000','MM/DD/YYYY'))
 PARTITION sales_feb2000 VALUES LESS THAN(TO_DATE('03/01/2000','MM/DD/YYYY'))
 PARTITION sales_mar2000 VALUES LESS THAN(TO_DATE('04/01/2000','MM/DD/YYYY'))
 PARTITION sales_apr2000 VALUES LESS THAN(TO_DATE('05/01/2000','MM/DD/YYYY'))
 PARTITION sales_may2000 VALUES LESS THAN(TO_DATE('06/01/2000','MM/DD/YYYY')));

This statement creates a table sales_composite that is range partitioned on the sales_date field and hash subpartitioned on salesman_id. When you use a template, Oracle names the subpartitions by concatenating the partition name, an underscore, and the subpartition name from the template. Oracle places this subpartition in the tablespace specified in the template. In the previous statement, sales_jan2000_sp1 is created and placed in tablespace ts1 while sales_jan2000_sp4 is created and placed in tablespace ts4. In the same manner, sales_apr2000_sp1 is created and placed in tablespace ts1 while sales_apr2000_sp4 is created and placed in tablespace ts4. Figure 18-4 offers a graphical view of the previous example.

Figure 18-4 Composite Range-Hash Partitioning

Description of "Figure 18-4 Composite Range-Hash Partitioning"

Composite Partitioning Range-List Example

CREATE TABLE bimonthly_regional_sales
(deptno NUMBER, 
 item_no VARCHAR2(20),
 txn_date DATE, 
 txn_amount NUMBER, 
 state VARCHAR2(2))
PARTITION BY RANGE (txn_date)
SUBPARTITION BY LIST (state)
SUBPARTITION TEMPLATE(

SUBPARTITION east VALUES('NY', 'VA', 'FL') TABLESPACE ts1,
SUBPARTITION west VALUES('CA', 'OR', 'HI') TABLESPACE ts2,
SUBPARTITION central VALUES('IL', 'TX', 'MO') TABLESPACE ts3)

(
PARTITION janfeb_2000 VALUES LESS THAN (TO_DATE('1-MAR-2000','DD-MON-YYYY')),
PARTITION marapr_2000 VALUES LESS THAN (TO_DATE('1-MAY-2000','DD-MON-YYYY')),
PARTITION mayjun_2000 VALUES LESS THAN (TO_DATE('1-JUL-2000','DD-MON-YYYY'))
);

This statement creates a table bimonthly_regional_sales that is range partitioned on the txn_date field and list subpartitioned on state. When you use a template, Oracle names the subpartitions by concatenating the partition name, an underscore, and the subpartition name from the template. Oracle places this subpartition in the tablespace specified in the template. In the previous statement, janfeb_2000_east is created and placed in tablespace ts1 while janfeb_2000_central is created and placed in tablespace ts3. In the same manner, mayjun_2000_east is placed in tablespace ts1 while mayjun_2000_central is placed in tablespace ts3. Figure 18-5 offers a graphical view of the table bimonthly_regional_sales and its 9 individual subpartitions.

Figure 18-5 Composite Range-List Partitioning

Description of "Figure 18-5 Composite Range-List Partitioning"

When to Partition a Table

Here are some suggestions for when to partition a table:

• Tables greater than 2GB should always be considered for partitioning.

• Tables containing historical data, in which new data is added into the newest partition. A typical example is a historical table where only the current month's data is updatable and the other 11 months are read only.

Local Partitioned Indexes

Local partitioned indexes are easier to manage than other types of partitioned indexes. They also offer greater availability and are common in DSS environments. The reason for this is equipartitioning: each partition of a local index is associated with exactly one partition of the table. This enables Oracle to automatically keep the index partitions in sync with the table partitions, and makes each table-index pair independent. Any actions that make one partition's data invalid or unavailable only affect a single partition.

Local partitioned indexes support more availability when there are partition or subpartition maintenance operations on the table. A type of index called a local nonprefixed index is very useful for historical databases. In this type of index, the partitioning is not on the left prefix of the index columns.

You cannot explicitly add a partition to a local index. Instead, new partitions are added to local indexes only when you add a partition to the underlying table. Likewise, you cannot explicitly drop a partition from a local index. Instead, local index partitions are dropped only when you drop a partition from the underlying table.

A local index can be unique. However, in order for a local index to be unique, the partitioning key of the table must be part of the index's key columns. Unique local indexes are useful for OLTP environments.

Figure 18-6 offers a graphical view of local partitioned indexes.

Figure 18-6 Local Partitioned Index

Description of "Figure 18-6 Local Partitioned Index"

Global Partitioned Indexes

Oracle offers two types of global partitioned index: range partitioned and hash partitioned.

Maintenance of Global Partitioned Indexes

By default, the following operations on partitions on a heap-organized table mark all global indexes as unusable:

ADD (HASH) 
COALESCE (HASH) 
DROP 
EXCHANGE 
MERGE 
MOVE 
SPLIT 
TRUNCATE 

These indexes can be maintained by appending the clause UPDATE INDEXES to the SQL statements for the operation. The two advantages to maintaining global indexes:

• The index remains available and online throughout the operation. Hence no other applications are affected by this operation.

• The index doesn't have to be rebuilt after the operation.

Example:

ALTER TABLE DROP PARTITION P1 UPDATE INDEXES;

Note: This feature is supported only for heap-organized tables.

See Also: Oracle Database SQL Reference for more information about the UPDATE INDEXES clause

Figure 18-7 offers a graphical view of global partitioned indexes.

Figure 18-7 Global Partitioned Index

Description of "Figure 18-7 Global Partitioned Index"

Global Nonpartitioned Indexes

Global nonpartitioned indexes behave just like a nonpartitioned index. They are commonly used in OLTP environments and offer efficient access to any individual record.

Figure 18-8 offers a graphical view of global nonpartitioned indexes.

Figure 18-8 Global Nonpartitioned Index

Description of "Figure 18-8 Global Nonpartitioned Index"

Miscellaneous Information about Creating Indexes on Partitioned Tables

You can create bitmap indexes on partitioned tables, with the restriction that the bitmap indexes must be local to the partitioned table. They cannot be global indexes.

Global indexes can be unique. Local indexes can only be unique if the partitioning key is a part of the index key.

Using Partitioned Indexes in OLTP Applications

Here are a few guidelines for OLTP applications:

• Global indexes and unique, local indexes provide better performance than nonunique local indexes because they minimize the number of index partition probes.

• Local indexes offer better availability when there are partition or subpartition maintenance operations on the table.

• Hash-partitioned global indexes offer better performance by spreading out contention when the index is monotonically growing. In other words, most of the index insertions occur only on the right edge of an index.

Using Partitioned Indexes in Data Warehousing and DSS Applications

Here are a few guidelines for data warehousing and DSS applications:

• Local indexes are preferable because they are easier to manage during data loads and during partition-maintenance operations.

• Local indexes can improve performance because many index partitions can be scanned in parallel by range queries on the index key.

Partitioned Indexes on Composite Partitions

Here are a few points to remember when using partitioned indexes on composite partitions:

• Subpartitioned indexes are always local and stored with the table subpartition by default.

• Tablespaces can be specified at either index or index subpartition levels.

Partition Pruning

The Oracle database server explicitly recognizes partitions and subpartitions. It then optimizes SQL statements to mark the partitions or subpartitions that need to be accessed and eliminates (prunes) unnecessary partitions or subpartitions from access by those SQL statements. In other words, partition pruning is the skipping of unnecessary index and data partitions or subpartitions in a query.

For each SQL statement, depending on the selection criteria specified, unneeded partitions or subpartitions can be eliminated. For example, if a query only involves March sales data, then there is no need to retrieve data for the remaining eleven months. Such intelligent pruning can dramatically reduce the data volume, resulting in substantial improvements in query performance.

If the optimizer determines that the selection criteria used for pruning are satisfied by all the rows in the accessed partition or subpartition, it removes those criteria from the predicate list (WHERE clause) during evaluation in order to improve performance. However, the optimizer cannot prune partitions if the SQL statement applies a function to the partitioning column (with the exception of the TO_DATE function). Similarly, the optimizer cannot use an index if the SQL statement applies a function to the indexed column, unless it is a function-based index.

Pruning can eliminate index partitions even when the underlying table's partitions cannot be eliminated, but only when the index and table are partitioned on different columns. You can often improve the performance of operations on large tables by creating partitioned indexes that reduce the amount of data that your SQL statements need to access or modify.

Equality, range, LIKE, and IN-list predicates are considered for partition pruning with range or list partitioning, and equality and IN-list predicates are considered for partition pruning with hash partitioning.

SELECT SUM(value)
FROM cust_orders
WHERE order_date BETWEEN '28-MAR-98' AND '23-APR-98';

Partition-wise Joins

A partition-wise join is a join optimization for joining two tables that are both partitioned along the join column(s). With partition-wise joins, the join operation is broken into smaller joins that are performed sequentially or in parallel. Another way of looking at partition-wise joins is that they minimize the amount of data exchanged among parallel slaves during the execution of parallel joins by taking into account data distribution.

Parallel DML

Parallel execution dramatically reduces response time for data-intensive operations on large databases typically associated with decision support systems and data warehouses. In addition to conventional tables, you can use parallel query and parallel DML with range- and hash-partitioned tables. By doing so, you can enhance scalability and performance for batch operations.

The semantics and restrictions for parallel DML sessions are the same whether you are using index-organized tables or not.