List Partitioning
List partitioning can explicitly control how rows are mapped to partitions by specifying a list of discrete values for the partition key in the description for each partition. The advantages of list partitioning are that data can be partitioned by enumerating partition values, and unordered and irrelevant datasets can be grouped and organized. For partition key values that are not defined in the list, you can use the default partition (DEFAULT) to save data. In this way, all rows that are not mapped to any other partition do not generate errors. Example:
gaussdb=# CREATE TABLE bmsql_order_line ( ol_w_id INTEGER NOT NULL, ol_d_id INTEGER NOT NULL, ol_o_id INTEGER NOT NULL, ol_number INTEGER NOT NULL, ol_i_id INTEGER NOT NULL, ol_delivery_d TIMESTAMP, ol_amount DECIMAL(6,2), ol_supply_w_id INTEGER, ol_quantity INTEGER, ol_dist_info CHAR(24) ) PARTITION BY LIST(ol_d_id) ( PARTITION p0 VALUES (1,4,7), PARTITION p1 VALUES (2,5,8), PARTITION p2 VALUES (3,6,9), PARTITION p3 VALUES (DEFAULT) ); gaussdb=# DROP TABLE bmsql_order_line;
The preceding example is similar to that of hash partitioning. The ol_d_id column is used for partitioning. However, list partitioning limits a possible range of ol_d_id values, and data that is not in the list enters the p3 partition (DEFAULT). Compared with hash partitioning, list partitioning has better control over partition keys and can accurately store target data in the expected partitions. However, if there are a large number of list values, it is difficult to define partitions. In this case, hash partitioning is recommended. List partitioning and hash partitioning are used to group and organize unordered and irrelevant datasets.
List partitioning supports a partition key with up to 16 columns. For one-column partition keys, the enumerated values in the list cannot be NULL during partition defining. For multi-column partition keys, the enumerated values in the list can be NULL during partition defining.
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