Materialized Views Willie Albino May 15 2003 Materialized

Materialized Views Willie Albino May 15, 2003

Materialized Views – Agenda l What is a Materialized View? – l How Materialized Views Work – l – Syntax, Refresh Modes/Options, Build Methods Examples Dimensions – – 2 Parameter Settings, Privileges, Query Rewrite Creating Materialized Views – l Advantages and Disadvantages What are they? Examples Willie Albino May 15, 2003

What is a Materialized View? l A database object that stores the results of a query – l Features/Capabilities – – – 3 Marries the query rewrite features found in Oracle Discoverer with the data refresh capabilities of snapshots Can be partitioned and indexed Can be queried directly Can have DML applied against it Several refresh options are available Best in read-intensive environments Willie Albino May 15, 2003

Advantages and Disadvantages l Advantages – – – Useful for summarizing, pre-computing, replicating and distributing data Faster access for expensive and complex joins Transparent to end-users l l Disadvantages – – 4 MVs can be added/dropped without invalidating coded SQL Performance costs of maintaining the views Storage costs of maintaining the views Willie Albino May 15, 2003

Database Parameter Settings l init. ora parameter – l System or session settings – – l – 5 query_rewrite_enabled={true|false} query_rewrite_integrity= {enforced|trusted|stale_tolerated} Can be set for a session using – l COMPATIBLE=8. 1. 0 (or above) alter session set query_rewrite_enabled=true; alter session set query_rewrite_integrity=enforced; Privileges which must be granted to users directly – QUERY_REWRITE - for MV using objects in own schema – GLOBAL_QUERY_REWRITE - for objects in other schemas Willie Albino May 15, 2003

Query Rewrite Details l query_rewrite_integrity Settings: – enforced – rewrites based on Oracle enforced constraints l – trusted – rewrites based on Oracle enforced constraints and known, but not enforced, data relationships l l l – Primary key, foreign keys Data dictionary information Dimensions stale_tolerated – queries rewritten even if Oracle knows the mv’s data is out-of-sync with the detail data l 6 Primary key, foreign keys Data dictionary information Willie Albino May 15, 2003

Query Rewrite Details (cont’d) l Query Rewrite Methods – Full Exact Text Match l – Partial Text Match l l – All required data must be present in the MV or retrievable through a join-back operation Join Compatibility l 7 Compares text starting at FROM clause SELECT clause must be satisfied for rewrite to occur Data Sufficiency l – Friendlier/more flexible version of text matching All joined columns are present in the MV Willie Albino May 15, 2003

Query Rewrite Details (cont’d) – Grouping Compatibility l l – Allows for matches in groupings at higher levels than those defined MV query Required if both query and MV contain a GROUP BY clause Aggregate Compatibility l Allows for interesting rewrites of aggregations – 8 If SUM(x) and COUNT(x) are in MV, the MV may be used if the query specifies AVG(x) Willie Albino May 15, 2003

Syntax For Materialized Views CREATE MATERIALIZED VIEW <name> TABLESPACE <tbs name> {<storage parameters>} <build option> REFRESH <refresh option> <refresh mode> [ENABLE|DISABLE] QUERY REWRITE AS SELECT <select clause>; l. The <build option> determines when MV is built – BUILD IMMEDIATE: view is built at creation time – BUILD DEFFERED: view is built at a later time – ON PREBUILT TABLE: use an existing table as view source l. Must set QUERY_REWRITE_INTEGRITY to TRUSTED 9 Willie Albino May 15, 2003

Materialized View Refresh Options l Refresh Options – COMPLETE – totally refreshes the view l – FAST – incrementally applies data changes l l l – A materialized view log is required on each detail table Data changes are recorded in MV logs or direct loader logs Many other requirements must be met for fast refreshes FORCE – does a FAST refresh in favor of a COMPLETE l 10 Can be done at any time; can be time consuming The default refresh option Willie Albino May 15, 2003

Materialized View Refresh Modes l Refresh Modes – ON COMMIT – refreshes occur whenever a commit is performed on one of the view’s underlying detail table(s) l l l – Available only with single table aggregate or join based views Keeps view data transactionally accurate Need to check alert log for view creation errors ON DEMAND – refreshes are initiated manually using one of the procedures in the DBMS_MVIEW package l l Can be used with all types of materialized views Manual Refresh Procedures – DBMS_MVIEW. REFRESH(<mv_name>, <refresh_option>) – DBMS_MVIEW. REFRESH_ALL_MVIEWS() – 11 START WITH [NEXT] <date> - refreshes start at a specified date/time and continue at regular intervals Willie Albino May 15, 2003

Materialized View Example CREATE MATERIALIZED VIEW items_summary_mv ON PREBUILT TABLE REFRESH FORCE SELECT AS a. PRD_ID, a. SITE_ID, a. TYPE_CODE, a. CATEG_ID, sum(a. GMS) GMS, sum(a. NET_REV) NET_REV, sum(a. BOLD_FEE) BOLD_FEE, sum(a. BIN_PRICE) BIN_PRICE, sum(a. GLRY_FEE) GLRY_FEE, sum(a. QTY_SOLD) QTY_SOLD, count(a. ITEM_ID) UNITS FROM items a GROUP BY a. PRD_ID, a. SITE_ID, a. TYPE_CODE, a. CATEG_ID; ANALYZE TABLE item_summary_mv COMPUTE STATISTICS; 12 Willie Albino May 15, 2003

Materialized View Example (cont’d) -- Query to test impact of materialized view select categ_id, site_id, sum(net_rev), sum(bold_fee), count(item_id) from items where prd_id in ('2000 M 05', '2000 M 06', '2001 M 07', '2001 M 08') and site_id in (0, 1) and categ_id in (2, 4, 6, 8, 1, 22) group by categ_id, site_id save mv_example. sql 13 Willie Albino May 15, 2003

Materialized View Example (cont’d) SQL> ALTER SESSION SET QUERY_REWRITE_INTEGRITY= TRUSTED; SQL> ALTER SESSION SET QUERY_REWRITE_ENABLED= FALSE; SQL> @mv_example. sql CATEG_ID SITE_ID SUM(NET_REV) SUM(BOLD_FEE) COUNT(ITEM_ID) -----------------1 0 -2. 35 0 1 22 0 -42120. 87 -306 28085 Elapsed: 01: 32: 17. 93 Execution Plan -----------------------------0 SELECT STATEMENT Optimizer=HINT: FIRST_ROWS (Cost=360829 Card=6 Bytes=120) 1 0 SORT (GROUP BY) (Cost=360829 Card=6 Bytes=120) 2 1 PARTITION RANGE (INLIST 3 2 TABLE ACCESS (FULL) OF ‘ITEMS' (Cost=360077 Card=375154 Bytes=7503080) 14 Willie Albino May 15, 2003

Materialized View Example (cont’d) SQL> ALTER SESSION SET QUERY_REWRITE_ENABLED= TRUE; SQL> @mv_example. sql CATEG_ID SITE_ID SUM(NET_REV) SUM(BOLD_FEE) COUNT(ITEM_ID) -----------------1 0 -2. 35 0 1 22 0 -42120. 87 -306 28085 Elapsed: 00: 01: 40. 47 Execution Plan ----------------------------------------------- 0 SELECT STATEMENT Optimizer=HINT: FIRST_ROWS (Cost=3749 Card=12 Bytes=276) 1 0 2 1 3 2 SORT (GROUP BY) (Cost=3749 Card=12 Bytes=276) PARTITION RANGE (INLIST) TABLE ACCESS (FULL) OF ‘ITEMS_SUMMARY_MV' (Cost=3723 Card=7331 Bytes=168613) 15 Willie Albino May 15, 2003

Example of FAST REFRESH MV CREATE MATERIALIZED VIEW LOG ON ITEMS TABLESPACE MV_LOGS STORAGE(INITIAL 10 M NEXT 10 M) WITH ROWID; CREATE MATERIALIZED VIEW LOG ON CUSTOMERS TABLESPACE MV_LOGS STORAGE(INITIAL 1 M NEXT 1 M) WITH ROWID; CREATE MATERIALIZED VIEW cust_activity BUILD IMMEDIATE REFRESH FAST ON COMMIT AS SELECT u. ROWID cust_rowid, l. ROWID item_rowid, u. cust_id, u. custname, u. email, l. categ_id, l. site_id, sum(gms), sum(net_rev_fee) FROM customers u, items l WHERE u. cust_id = l. seller_id GROUP BY u. cust_id, u. custname, u. email, l. categ_id, l. site_id; 16 Willie Albino May 15, 2003

Getting Information About an MV Getting information about the key columns of a materialized view: SELECT POSITION_IN_SELECT POSITION, CONTAINER_COLUMN, DETAILOBJ_OWNER, DETAILOBJ_NAME SOURCE, DETAILOBJ_ALIAS, DETAILOBJ_TYPE, DETAILOBJ_COLUMN SRC_COLUMN FROM USER_MVIEW_KEYS WHERE MVIEW_NAME=‘ITEMS_SUMMARY_MV’; POS COLUMN OWNER SOURCE ALIAS TYPE SRC_COLUMN ---------- ------ 17 1 PRD_ID TAZ ITEMS A TABLE PRD_ID 2 SITE_ID TAZ ITEMS A TABLE SITE_ID 3 TYPE_CODE TAZ ITEMS A TABLE TYPE_CODE 4 CATEG_ID TAZ ITEMS A TABLE CATEG_ID Willie Albino May 15, 2003

Getting Information About an MV Getting information about the aggregate columns of a materialized view: SELECT POSITION_IN_SELECT CONTAINER_COLUMN POSITION, COLUMN, AGG_FUNCTION FROM USER_MVIEW_AGGREGATES WHERE MVIEW_NAME=‘ITEMS_SUMMARY_MV’; POSITION COLUMN AGG_FUNCTION ------------- 6 GMS SUM 7 NET_REV SUM : 18 : : 11 QTY_SOLD SUM 12 UNITS COUNT Willie Albino May 15, 2003

Dimensions l A way of describing complex data relationships – – Used to perform query rewrites, but not required Defines hierarchical relationships between pairs of columns l l l 19 Hierarchies can have multiple levels Each child in the hierarchy has one and only one parent Each level key can identify one or more attribute Child join keys must be NOT NULL Dimensions should be validated using the DBMS_OLAP. VALIDATE_DIMENSION package – Bad row ROWIDs stored in table: mview$_exceptions Willie Albino May 15, 2003

Syntax For Creating A Dimension CREATE DIMENSION <dimension name> LEVEL [<level> IS <level_table. level_column>…] HIERARCHY <hierarchy_name> ( <child_level> CHILD OF <parent_level>…] ATTRIBUTE <level> DETERMINES <dependent_column>, …); To validate a dimension: exec dbms_olap. validate_dimension(<dim_name>, <owner>, FALSE); 20 Willie Albino May 15, 2003

Example of Creating A Dimension CREATE DIMENSION time_dim LEVEL CAL_DATE IS calendar. CAL_DATE LEVEL PRD_ID IS calendar. PRD_ID LEVEL QTR_ID IS calendar. QTR_ID LEVEL YEAR_ID IS calendar. YEAR_ID LEVEL WEEK_IN_YEAR_ID IS calendar. WEEK_IN_YEAR_ID HIERARCHY calendar_rollup (CAL_DATE CHILD OF PRD_ID CHILD OF QTR_ID CHILD OF YEAR_ID) HIERARCHY week_rollup (CAL_DATE CHILD OF WEEK_IN_YEAR_ID CHILD OF YEAR_ID) ATTRIBUTE PRD_ID DETERMINES PRD_DESC ATTRIBUTE QTR_ID DETERMINES QTR_DESC; 21 Willie Albino May 15, 2003

Example of Validating A Dimension SQL> exec dbms_olap. validate_dimension(‘time_dim’, USER, FALSE); PL/SQL procedure successfully completed. SQL> select * from mview$_exceptions; no rows selected. -- Main cause of errors is a child level having multiple parents -- If above query returns rows, the bad rows can be found as follows: select * from calendar where rowid in (select bad_rowid from mview$_exceptions); 22 Willie Albino May 15, 2003

Example of Using Dimensions -- Step 1 of 4 -- Create materialized view (join-aggregate type) CREATE MATERIALIZED VIEW items_ mv BUILD IMMEDIATE REFRESH ON DEMAND ENABLE QUERY REWRITE AS SELECT l. slr_id , c. cal_date, sum(l. gms) gms FROM items l, calendar c WHERE l. end_date=c. cal_date GROUP BY l. slr_id, c. cal_date; 23 Willie Albino May 15, 2003

Example of Using Dimensions (cont’d) -- Step 2 of 4: (not really required, for demonstration only) -- Execute query based on “quarter”, not “date”, without a time dimension -- Note that the detail tables are accessed SQL> 2 3 4 select c. qtr_id, sum(l. gms) gms from items l, calendar c where l. end_date=c. cal_date group by l. slr_id, c. qtr_id; Execution Plan -----------------------------SELECT STATEMENT Optimizer=CHOOSE (Cost=16174 Card=36258 Bytes=1160256) SORT (GROUP BY) (Cost=16174 Card=36258 Bytes=1160256) HASH JOIN (Cost=81 Card=5611339 Bytes=179562848) TABLE ACCESS (FULL) OF ’CALENDAR' (Cost=2 Card=8017 Bytes=128272) TABLE ACCESS (FULL) OF ’ITEMS' (Cost=76 Card=69993 Bytes=1119888) 24 Willie Albino May 15, 2003

Example of Using Dimensions (cont’d) -- Step 3 of 4: Create time dimension (see slide #21 for SQL) @cr_time_dim. sql Dimension Created -- Step 4 of 4: Rerun query based on “quarter” with time dimension SQL> 2 3 4 select c. qtr_id, sum(l. gms) gms from items l, calendar c where l. end_date=c. cal_date group by l. slr_id, c. qtr_id; Execution Plan -----------------------------SELECT STATEMENT Optimizer=CHOOSE (Cost=3703 Card=878824 Bytes=44820024) SORT (GROUP BY) (Cost=3703 Card=878824 Bytes=44820024) HASH JOIN (Cost=31 Card=878824 Bytes=44820024) VIEW (Cost=25 Card=8017 Bytes=128272) SORT (UNIQUE) (Cost=25 Card=8017 Bytes=128272) TABLE ACCESS (FULL) OF ‘CALENDAR’ (Cost=2 Card=8017 Bytes=128272) TABLE ACCESS (FULL) OF ‘ITEMS_MV’ (Cost=3 Card=10962 Bytes=383670) 25 Willie Albino May 15, 2003

Summary l Materialized Views – – – l Dimensions – 26 reduce system cpu/io resource requirements by precalculating and storing results of intensive queries allow for the automatic rewriting of intensive queries are transparent to the application have storage/maintenance requirements can understand complex data relationships can be refreshed on demand or on a schedule allow you to “tell” Oracle about complex data relationships which can be used to rewrite queries Willie Albino May 15, 2003

References l Using Oracle 9 i Materialized Views (Technet Oracle By Example) – l Oracle Expert-One-On-One – Thomas Kyte l The Secrets of Materialized Views – l l http: //www. akadia. com/services/ora_materialized_views. html OLAP DB-Design with Dimensions – http: //www. akadia. com/services/ora_olap_dimensions. html The Secrets of Dimensions – 27 http: //technet. oracle. com/products/oracle 9 i/htdocs/9 iober 2/obe 9 ir 2/obedwh/html/mv/mv. htm http: //www. akadia. com/services/ora_dimensions. html Willie Albino May 15, 2003

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Requirements for FAST REFRESH 29 Willie Albino May 15, 2003

Rqmts For FAST REFRESH (cont’d) 30 Willie Albino May 15, 2003