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), how I might cluster rows together that are subject to updates, and what I might do if I just get too many updates to handle. The fastest way to update every row in the table is to rebuild the table from scratch. Case 2 is common in Data Warehouses and overnight batch jobs. I worry about how ETL tools apply updates (did you know Data Stage applys updates singly, but batches inserts in arrays? The two most common forms of Bulk Updates are: Case 1 is uninteresting.What I love about writing SQL Tuning articles is that I very rarely end up publishing the findings I set out to achieve. We have a table containing years worth of data, most of which is static; we are updating selected rows that were recently inserted and are still volatile. For the purposes of the test, we will assume that the target table of the update is arbitrarily large, and we want to avoid things like full-scans and index rebuilds.With this one, I set out to demonstrate the advantages of PARALLEL DML, didn't find what I thought I would, and ended up testing 8 different techniques to find out how they differed. The methods covered include both PL/SQL and SQL approaches.It's a bit of a kludge, but we can do this in PL/SQL using a Parallel Enable Table Function. COUNT; END LOOP; CLOSE test_cur; COMMIT; PIPE ROW(cnt); RETURN; END; / Note that it receives its data via a Ref Cursor parameter. FK RUN 1 RUN 2 ----------------------------------- ----- ----- 1. A single bitmap index has added around 10% to the overall runtime of PL/SQL solutions, whereas the set-based (SQL-based) solutions run faster than the B-Tree indexes case (above).Here's the function: CREATE OR REPLACE FUNCTION test_parallel_update ( test_cur IN SYS_REFCURSOR ) RETURN test_num_arr PARALLEL_ENABLE (PARTITION test_cur BY ANY) PIPELINED IS PRAGMA AUTONOMOUS_TRANSACTION; test_rec TEST%ROWTYPE; TYPE num_tab_t IS TABLE OF NUMBER(38); TYPE vc2_tab_t IS TABLE OF VARCHAR2(4000); pk_tab NUM_TAB_T; fk_tab NUM_TAB_T; fill_tab VC2_TAB_T; cnt INTEGER := 0; BEGIN LOOP FETCH test_cur BULK COLLECT INTO pk_tab, fk_tab, fill_tab LIMIT 1000; EXIT WHEN pk_tab. This is a feature of Oracle's parallel-enabled functions; they will apportion the rows of a single Ref Cursor amongst many parallel slaves, with each slave running over a different subset of the input data set. Parallel PL/SQL 28.2 27.2 6.3 Let's see how a Foreign Key constraint affects things. Although not shown here, this effect is magnified with each additional bitmap index. I spend an inordinate proportion of design time of an ETL system worrying about the relative proportion of rows inserted vs updated.I want to test on a level playing field and remove special factors that unfairly favour one method, so there are some rules: TEST (Update Source) - 100K rows TEST (Update target) - 10M rows Name Type Name Type ------------------------------ ------------ ------------------------------ ------------ PK NUMBER PK NUMBER FK NUMBER FK NUMBER FILL VARCHAR2(40) FILL VARCHAR2(40) Not many people code this way, but there are some Pro*C programmers out there who are used to Explicit Cursor Loops (OPEN, FETCH and CLOSE commands) and translate these techniques directly to PL/SQL.

Looking more closely at the trace files I suspect that the PARALLEL DML used 128 readers and 128 writers, although it hard to be sure.

Although the number of physical disk blocks and Current Mode Gets are about the same in each test, the Hash Join method performs multi-block reads, resulting in fewer visits to the disk.

All 8 methods above were benchmarked on the assumption that the target table is arbitrarily large and the subset of rows/blocks to be updated are relatively small.

If the proportion of updated blocks increases, then the average cost of finding those rows decreases; the exercise becomes one of tuning the data access rather than tuning the update.

Why is the Parallel PL/SQL (Method 8) approach much faster than the Parallel DML MERGE (Method 7)? Below we see the trace from the Parallel Coordinator session of Method 7: MERGE /* first_rows */ INTO test USING test5 new ON (= new.pk) WHEN MATCHED THEN UPDATE SET fk = , fill = call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- ---------- ---------- Parse 1 0.02 0.02 0 4 1 0 Execute 1 1.85 57.91 1 7 2 100000 Fetch 0 0.00 0.00 0 0 0 0 ------- ------ -------- ---------- ---------- ---------- ---------- ---------- total 2 1.87 57.94 1 11 3 100000 Misses in library cache during parse: 1 Optimizer mode: FIRST_ROWS Parsing user id: 140 Rows Row Source Operation ------- --------------------------------------------------- 128 PX COORDINATOR (cr=7 pr=1 pw=0 time=57912088 us) 0 PX SEND QC (RANDOM) : TQ10002 (cr=0 pr=0 pw=0 time=0 us) 0 INDEX MAINTENANCE TEST (cr=0 pr=0 pw=0 time=0 us)(object id 0) 0 PX RECEIVE (cr=0 pr=0 pw=0 time=0 us) 0 PX SEND RANGE : TQ10001 (cr=0 pr=0 pw=0 time=0 us) 0 MERGE TEST (cr=0 pr=0 pw=0 time=0 us) 0 PX RECEIVE (cr=0 pr=0 pw=0 time=0 us) 0 PX SEND HYBRID (ROWID PKEY) : TQ10000 (cr=0 pr=0 pw=0 time=0 us) 0 VIEW (cr=0 pr=0 pw=0 time=0 us) 0 NESTED LOOPS (cr=0 pr=0 pw=0 time=0 us) 0 PX BLOCK ITERATOR (cr=0 pr=0 pw=0 time=0 us) 0 TABLE ACCESS FULL TEST5 (cr=0 pr=0 pw=0 time=0 us) 0 TABLE ACCESS BY INDEX ROWID TEST (cr=0 pr=0 pw=0 time=0 us) 0 INDEX UNIQUE SCAN TEST_PK (cr=0 pr=0 pw=0 time=0 us)(object id 141439) Elapsed times include waiting on following events: Event waited on Times Max.

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