The following example shows the creation of a system partitioned table.
INSERT INTO system_partitioned_tab VALUES (1, 'ONE', 'One', SYSDATE); * ERROR at line 1: ORA-14701: partition-extended name or bind variable must be used for DMLs on tables partitioned by the System method SQL INSERT INTO system_partitioned_tab PARTITION (part_1) VALUES (1, 'ONE', 'One', SYSDATE); INSERT INTO system_partitioned_tab PARTITION (part_2) VALUES (2, 'TWO', 'Two', SYSDATE); COMMIT; EXEC DBMS_STATS.gather_table_stats(USER, 'SYSTEM_PARTITIONED_TAB'); COLUMN table_name FORMAT A25 COLUMN partition_name FORMAT A20 COLUMN high_value FORMAT A10 SELECT table_name, partition_name, high_value, num_rows FROM user_tab_partitions ORDER BY table_name, partition_name; TABLE_NAME PARTITION_NAME HIGH_VALUE NUM_ROWS ------------------------- -------------------- ---------- ---------- SYSTEM_PARTITIONED_TAB PART_1 1 SYSTEM_PARTITIONED_TAB PART_2 1 2 rows selected.
Updating partition key column
We denormalize because the goal in Cassandra is to execute the read operations of the use case with one query or, more preferably, one disk I/O.
To illustrate the use case, the data model and the operations I will use an example about teams and team members of Formula 1 racing teams.
Because of this indeterminacy, referencing other tables only within sub-selects is safer, though often harder to read and slower than using a join.
Execution of Attempt to insert a new stock item along with the quantity of stock.
In my previous blogpost “Analysis of the impact of new use cases on a Cassandra data model and operations in code” I looked at the risks of deploying Cassandra.
In Cassandra we face the challenges of data modelling and designing the CRUD operations (create, read, update, delete), because in Cassandra it is best practice to denormalize the data models.
SQL INSERT INTO interval_tab VALUES (7, 'SEVEN', 'Seven', TO_DATE('01-DEC-2007', 'DD-MON-YYYY')); INSERT INTO interval_tab VALUES (8, 'EIGHT', 'Eight', TO_DATE('31-DEC-2007', 'DD-MON-YYYY')); COMMIT; EXEC DBMS_STATS.gather_table_stats(USER, 'INTERVAL_TAB'); SELECT table_name, partition_name, high_value, num_rows FROM user_tab_partitions ORDER BY table_name, partition_name; TABLE_NAME PARTITION_NAME HIGH_VALUE NUM_ROWS -------------------- -------------------- ---------------------------------------- ---------- INTERVAL_TAB PART_01 TO_DATE(' 2007-11-01 ', 'SYYYY-M 2 M-DD HH24: MI: SS', 'NLS_CALENDAR=GREGORIA INTERVAL_TAB SYS_P44 TO_DATE(' 2007-12-01 ', 'SYYYY-M 2 M-DD HH24: MI: SS', 'NLS_CALENDAR=GREGORIA INTERVAL_TAB SYS_P45 TO_DATE(' 2008-02-01 ', 'SYYYY-M 2 M-DD HH24: MI: SS', 'NLS_CALENDAR=GREGORIA INTERVAL_TAB SYS_P46 TO_DATE(' 2008-01-01 ', 'SYYYY-M 2 M-DD HH24: MI: SS', 'NLS_CALENDAR=GREGORIA 4 rows selected.
SQL As you would expect, system partitioning allows large tables to be broken down into smaller partitions, but unlike other partitioning schemes, the database has no control over the placement of rows during insert operations.
Dynamo DB stores and retrieves each item based on the primary key value which must be unique.
Items are distributed across 10 GB storage units, called partitions (physical storage internal to Dynamo DB).
CREATE TABLE list_hash_tab ( id NUMBER, code VARCHAR2(10), description VARCHAR2(50), created_date DATE ) PARTITION BY LIST (code) SUBPARTITION BY HASH (id) ( PARTITION part_aa values ('AA') ( SUBPARTITION part_aa_01, SUBPARTITION part_aa_02 ), partition part_bb values ('BB') ( SUBPARTITION part_bb_01, SUBPARTITION part_bb_02 ) ); DECLARE l_code VARCHAR2(10); BEGIN FOR i IN 1 ..