Composite Partitioning

Oracle Composite Partitioning is a technique that allows you to combine two or more partitioning methods in a single table or index. This provides the flexibility to optimize data storage and performance for specific use cases that benefit from the strengths of different partitioning methods. Composite partitioning can be particularly useful when you want to partition by one method (such as range partitioning) and further subpartition the data using another method (like hash partitioning or list partitioning).

1. Overview of Composite Partitioning

Composite partitioning enables Oracle users to partition a table or index using multiple partitioning strategies in combination. It is typically used when one partitioning method does not fully optimize the workload, and a second method is required to fine-tune data distribution or performance.

In composite partitioning, the first partitioning method divides the data into major partitions, and the second partitioning method is applied to further divide those major partitions into subpartitions.

The most common types of composite partitioning are:

• Range-Hash Partitioning
• Range-List Partitioning
• List-Hash Partitioning

2. Why Use Composite Partitioning?

Composite partitioning provides several advantages:

• Fine-Grained Control: By using two partitioning methods, you get more control over how data is distributed and how queries are processed.
• Improved Parallelism: Using multiple partitioning schemes allows Oracle to handle larger data volumes and enables parallel execution across multiple partitions.
• Efficient Query Performance: Different queries can benefit from the strengths of different partitioning methods, particularly when querying large data sets that have a mix of range-based and hash-based access patterns.

3. Types of Composite Partitioning

1. Range-Hash Partitioning

In Range-Hash Partitioning, the data is first divided into partitions based on range values, and then each range partition is further divided using the hash partitioning method.

Example Use Case: If you have a large sales table where the sales data is first partitioned by year (range) and then further distributed across multiple partitions for load balancing, hash partitioning helps distribute the rows more evenly.

Example Syntax:

CREATE TABLE sales (

    sale_id NUMBER,

    sale_date DATE,

    amount NUMBER

)

PARTITION BY RANGE (sale_date)

SUBPARTITION BY HASH (sale_id)

SUBPARTITIONS 4 (

    PARTITION sales_2020 VALUES LESS THAN (TO_DATE('01-JAN-2021', 'DD-MON-YYYY')),

    PARTITION sales_2021 VALUES LESS THAN (TO_DATE('01-JAN-2022', 'DD-MON-YYYY'))

);

In this example:

• Range Partitioning divides the data by sale_date (for the years 2020 and 2021).
• Each year’s range partition is further hash subpartitioned by the sale_id column into 4 subpartitions.

2. Range-List Partitioning

In Range-List Partitioning, the data is divided into partitions by range values, and then each range partition is further divided using list partitioning. This allows more control over how specific lists of values are stored within each range partition.

Example Use Case: If you have a table with date ranges and a category column that can have multiple predefined values (like product categories), list partitioning within each range allows for fine-grained management of different categories within each time range.

Example Syntax:

CREATE TABLE sales (

    sale_id NUMBER,

    sale_date DATE,

    category VARCHAR2(20),

    amount NUMBER

)

PARTITION BY RANGE (sale_date)

SUBPARTITION BY LIST (category)

SUBPARTITIONS 4 (

    PARTITION sales_2020 VALUES LESS THAN (TO_DATE('01-JAN-2021', 'DD-MON-YYYY')),

    PARTITION sales_2021 VALUES LESS THAN (TO_DATE('01-JAN-2022', 'DD-MON-YYYY'))

);

In this example:

• Range Partitioning divides the data by sale_date (for the years 2020 and 2021).
• Each year’s range partition is further list subpartitioned by the category column into 4 subpartitions.

3. List-Hash Partitioning

In List-Hash Partitioning, the data is first divided into partitions based on list values (i.e., specific, predefined values), and then each list partition is further divided by hash partitioning. This combination is useful when you want to distribute data that has specific values (e.g., regions or types) across multiple partitions.

Example Use Case: For a sales table where the data is partitioned by predefined regions (list partitioning) and then further distributed evenly by hashing based on the sale amount.

Example Syntax:

CREATE TABLE sales (

    sale_id NUMBER,

    sale_date DATE,

    region VARCHAR2(20),

    amount NUMBER

)

PARTITION BY LIST (region)

SUBPARTITION BY HASH (amount)

SUBPARTITIONS 4 (

    PARTITION region_north VALUES ('North'),

    PARTITION region_south VALUES ('South'),

    PARTITION region_east VALUES ('East'),

    PARTITION region_west VALUES ('West')

);

In this example:

• List Partitioning divides the data by region into specific regions (North, South, East, West).
• Each region partition is further hash subpartitioned by the amount column into 4 subpartitions.

4. Benefits of Composite Partitioning

·        Improved Query Performance: Composite partitioning helps to improve query performance for complex queries that involve multiple filtering criteria. By combining partitioning strategies, Oracle can optimize query execution by narrowing the scope of partitions or subpartitions that need to be scanned.

·        Enhanced Parallelism: Different partitioning schemes can benefit parallel query execution. The hash partitioning in composite partitioning allows for parallel processing within each partition and subpartition, which can result in faster query execution.

·        More Control Over Data Distribution: By using composite partitioning, you have more control over how data is distributed. This is especially useful when there is no single partitioning strategy that works well across all use cases.

·        Efficient Data Management: You can manage and optimize storage for data with different access patterns. For instance, you can quickly access records for a specific range (via range partitioning) and also distribute them evenly across partitions (via hash or list partitioning).

5. Disadvantages of Composite Partitioning

·        Complexity: Composite partitioning adds complexity to table design and maintenance. It requires a good understanding of your data and how it is accessed in order to choose the most appropriate partitioning schemes.

·        Partition Pruning Limitations: Although composite partitioning helps in narrowing down the data set for certain types of queries, it still suffers from partition pruning limitations. Depending on the partitioning schemes used, Oracle may not always prune partitions as effectively as with a simple range or list partitioning method.

·        Overhead: The use of multiple partitioning methods can introduce some overhead in terms of both storage and query execution. This overhead must be carefully evaluated in the context of performance improvements.

6. Considerations for Choosing Partitioning Methods

• Range Partitioning is generally good for data with a natural range (e.g., dates, numeric intervals), where you want to divide data into discrete segments.
• List Partitioning works well for categorical data where you know the specific values in advance (e.g., regions, product categories).
• Hash Partitioning is ideal for ensuring even distribution of data, especially when there's no natural range or list to partition by.

Composite Partitioning is best used when:

• You need to combine the benefits of multiple partitioning methods.
• You have a complex data structure with multiple attributes that require different types of partitioning.
• You need to optimize data for parallel processing and load balancing across partitions.

7. Example Use Cases for Composite Partitioning

1.     Time-Series Data: For example, a sales table partitioned by year (range partitioning), and then further hash-partitioned by region to ensure even distribution across partitions.

2.     Geographical Data: If you have a region column with a fixed set of values (e.g., North, South, East, West), you might want to list partition by region and then hash partition based on sales data to balance the data across partitions.

3.     Product Data: You could range partition a product table based on product launch date and subpartition by category (list partitioning) or quantity (hash partitioning) for more efficient data management.

Summary of Composite Partitioning in Oracle

• Composite Partitioning combines multiple partitioning methods (range, list, and hash) for a single table or index.
• It provides fine-grained control over data distribution and query performance.
• It is particularly useful for complex data with multiple access patterns, allowing for optimal parallelism and performance.
• While it can offer significant performance improvements, composite partitioning introduces complexity in both design and maintenance.