Online flirten app Updating columns in oracle

The difference is that the Implicit Cursor internally performs bulk fetches, which should be faster than the Explicit Cursor because of the reduced context switches. I generally recommend against it for high-volume updates because the SET sub-query is nested, meaning it is performed once for each row updated. Using BULK COLLECT and FORALL statements is the new de-facto standard for PL/SQL programmers concerned about performance because it reduces context switching overheads between the PL/SQL and SQL engines.

updating columns in oracle-9

I am trying to update a table B geom column with the coordinates from table A.

Table A having coordinates in two separate columns x and y.

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.

The UPDATE portion of the code works in an identical fashion to the Implicit Cursor Loop, so this is not really a separate "UPDATE" method as such.

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.Table B to have the coordinates from Table A into Table B's geom spatial column.Table A and B have an ID in common for each record.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 (= 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.Wait Total Waited ---------------------------------------- Waited ---------- ------------ db file sequential read 1 0.02 0.02 reliable message 1 0.00 0.00 enq: RO - fast object reuse 1 0.00 0.00 os thread startup 256 0.09 23.61 PX Deq: Join ACK 7 0.00 0.00 PX Deq: Parse Reply 15 0.09 0.19 PX Deq Credit: send blkd 35 0.00 0.00 PX qref latch 5 0.00 0.00 PX Deq: Execute Reply 1141 1.96 30.30 SQL*Net message to client 1 0.00 0.00 SQL*Net message from client 1 0.05 0.05 We can see here that the Parallel Co-ordinator spent 23.61 seconds (of the 57.94 elapsed) simply starting up the parallel threads, and 30.3 seconds waiting for them to do their stuff.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.