CERN/IT/DB Oracle9i & VLDB Montse Collados Polidura, IT/DB Database Workshop - July 2001

CERN/IT/DB VLDB - Features  Partitioning  Materialized Views  Parallelism

CERN/IT/DB PARTITIONING DIVIDE AND CONQUER  Tables and indexes decomposed into smaller and more manageable pieces called partitions.  Same logical attributes, different physical attributes  Advantages:  Data management operations at the partition : data loads, index creation, backup/recovery, etc.  Improves query performance (partition pruning)  Possibility of concurrent maintenance operations on different partitions of the same table/index.  Partitioning can be implemented without requiring any modifications to your applications.

CERN/IT/DB Operations on partitions  Add a partition to an existing table  Split an existing partition into two partitions  Drop a partition  Load data into one table partition  Export data from one table partition  Import a table partition  Move a table partition - move it to another tablespace  Rename a partition  Truncate a table partition  Rebuild an index partition  Modify a partition - change the physical attributes of a partition

CERN/IT/DB How does it work? JAN2001FEB2001MAR2001 DEC2001 … Partitioned Table Insert into Table values (‘A’,’B’, ‘3-MAR-2001’, ‘C’) Select column1, column2 from Table where key = DEC2001;

CERN/IT/DB Types of Partitioning

CERN/IT/DB Range Partitioning CREATE TABLE event_data (event_id NUMBER(10), event_data BLOB) PARTITION BY RANGE(event_id) ( PARTITION event_ VALUES LESS THAN(100000) TABLESPACE tsa, PARTITION event_ VALUES LESS THAN(200000) TABLESPACE tsb, PARTITION event_ VALUES LESS THAN(300000) TABLESPACE tsc);

CERN/IT/DB Hash Partitioning CREATE TABLE event_data (event_idNUMBER(10), event_dataBLOB) PARTITION BY HASH(event_id) PARTITIONS 4 STORE IN (data1, data2, data3, data4);

CERN/IT/DB List Partitioning CREATE TABLE sales_list (salesman_idNUMBER(5), salesman_nameVARCHAR2(30), sales_stateVARCHAR2(20), sales_amountNUMBER(10), sales_dateDATE) PARTITION BY LIST(sales_state) ( PARTITION sales_west VALUES IN('California', 'Hawaii'), PARTITION sales_east VALUES IN ('New York', 'Virginia', 'Florida'), PARTITION sales_central VALUES IN('Texas', 'Illinois'), );

CERN/IT/DB Composite Partitioning CREATE TABLE event_data (event_id NUMBER(10), event_dataBLOB) PARTITION BY RANGE(event_id) SUBPARTITION BY HASH(event_id) SUBPARTITIONS 4 PARTITION event_ VALUES LESS THAN(100000) ( SUBPARTITION event_100000_1 TABLESPACE data1, SUBPARTITION event_100000_2 TABLESPACE data2, SUBPARTITION event_100000_3 TABLESPACE data3, SUBPARTITION event_100000_4 TABLESPACE data4), PARTITION event_ VALUES LESS THAN(200000), ( SUBPARTITION event_200000_1 TABLESPACE data1, SUBPARTITION event_200000_2 TABLESPACE data2, SUBPARTITION event_200000_3 TABLESPACE data3, SUBPARTITION event_200000_4 TABLESPACE data4);

CERN/IT/DB Partitioned Indexes  Local indexes, automatically linked to a table’s partitioning method.  Global indexes, partitioned independently.

CERN/IT/DB Materialized Views  Materialized views, also called snapshots, are schema objects that can be used to summarize, precompute, replicate, and distribute data.  Refreshed upon committing, on a recurring timed basis, or on demand.  Summary table: Query Rewrite mechanism.  Materialized views for distributed computing

CERN/IT/DB Parallelism  Server processes that perform parallel operations.  Degree of Parallelism (DOP) configured at database startup (PARALLEL_MIN_SERVERS, PARALLEL_MAX_SERVERS) and at the SQL statement level.

CERN/IT/DB Parallelism  Parallel execution improves processing:  Queries requiring large table scans and joins  Creation of large indexes  Partitioned index scans  Bulk inserts, updates, and deletes  Hardware considerations

CERN/IT/DB Conclusions  Divide & Conquer to gain performance and manageability.  Materialized Views to precompute and distribute data.  Parallel Execution to run faster.