Updating a file random access

Rated 4.55/5 based on 812 customer reviews

Even the idea create a temporary table holding only primary key and Column b, and then apply cursor to it is slow. Would u please help me how can i faster my procedure.u have given information 9i it same for oracle8i & dev6i.please help me Thank u very much for ur kind update information is really nice and working very insert----same prolem. That means -- just using math here -- that we have 600 seconds, 12,000 queries to run, 12000/60 = 20, so we are doing 20 per second -- or each query is taking 0.05 cpu seconds to run. do anything 12,000 times and you might have a problem tho! this might be one of the rare times that a temp table can be useful.

I am think of the way without using cursor, script as below. I don't understand what's the problem.i am going to give u full overview of my problem. The software is available in different portion of the country for data entry and report generation etc. What about: create global temporary table gtt ( id int primary key, cnt int ) on commit delete rows / you'll add that ONCE, it'll become part of your schema forever....

But the 2nd Where clause simply return the message of `more than one row is return', since the id is unpredictable and this create a `many to many' relationship in both tables. Many Thanks, (script) REM* the where-clause of the update cannot work UPDATE table b SET column_b1 = ( SELECT MAX(column_a1) FROM table_a a, table_b b WHERE a.id=GROUP BY a.id) WHERE table_IN (SELECT MIN(id) FROM table_a GROUP BY id); Your example is somewhat confusing -- you ask "update column a1 in table a where data in column b1 in table b" but your update shows you updating column b1 in table B with some data from table a. Every month the client office is to give data(NEW & EDITED) "BY DATE RANGWISE" to the headoffice in CD. Now, you "two step" it: insert into gtt select b.id, count(*) cnt from tabb b, taba a where = and a.cycle = b.cycle and b.site_id = 44 and b.rel_cd in ( 'code1', 'code2', 'code3' ) and b.groupid = '123' and is null group by / that gets all of the id/cnts for only the rows of interest.

Additionally -- given the way the where and set clauses are CODED in the above -- it would succeed. The Headoffice is merge the data into their system. For migration data first of all i create another temporary user named VISTEMP then cotinuing this kinds of code insert into VISTEMP. Now we can update the join: update ( select a.pop, from taba a, gtt b where = ) set pop = cnt / and thats it. Hi Tom, I’m selecting approximately 1 million records from some tables and populating another set of tables.

There are one column in each table, call id, to link them. --For incremental/New data----- insert into A select * from B where column_name NOT IN (select column_name from B); --For Edited Data------- cursore C_AB select * from B minus select * from A For R in C_AB loop Update A set....where ... this shows how I would approach getting the first two columns -- just add the other 2 and use merge to keep filling temp -- and then update the join: [email protected] -1 5 group by urefitem ) b 6 on (temp.urefitem = b.urefitem) 7 when matched then update set amount = b.sum_total 8 when not matched then insert (urefitem,amount) values ( b.urefitem, b.sum_total) 9 / 398 rows merged. using a cursor means you are back to "slow=very_true" you already WERE updating on a bulk basis??? But when I run the following query, it takes up 50% of CPU. tab A has these columns: id, cycle, pop tab B has these columns: id, cycle, site_id,rel_cd,groupid update tab A a set pop= (select count(*) from tab B b where b.id=and a.cycle = b.cycle and b.site_id=44 and b.rel_cd in('code1','code2','code3') and b.groupid='123') where pop is null and id in(select id from tab B); call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- ---------- ---------- Parse 1 0.00 0.00 0 0 0 0 Execute 2 496.35 499.54 7530955 9902630 76532 11444 Fetch 0 0.00 0.00 0 0 0 0 ------- ------ -------- ---------- ---------- ---------- ---------- ---------- total 3 496.35 499.54 7530955 9902630 76532 11444 Misses in library cache during parse: 0 Optimizer goal: CHOOSE Parsing user id: 305 Rows Row Source Operation ------- --------------------------------------------------- 1 UPDATE tab A 11445 MERGE JOIN 5942 VIEW VW_NSO_1 5942 SORT UNIQUE 31227 TABLE ACCESS FULL tab B 17385 SORT JOIN 12601 TABLE ACCESS FULL tab A Now my questions are: 1. We have several such updates that creates the same problems on the server from time to time and I would appreciate some guidance to resolve this.

updating a file random access-6

updating a file random access-47

SRAM is also used in personal computers, workstations, routers and peripheral equipment: CPU register files, internal CPU caches and external burst mode SRAM caches, hard disk buffers, router buffers, etc.

This may seem like a simple question: Update Column a1 in Table A with all data in Column b1 in Table B. I have a table named A containing say 100000 records. HSCODELIST 5 WHERE not exists 6 (SELECT NULL FROM VIStemp. Brao what I suggest then is not to do it in a single sql statement -- just proving that "there are exceptions to every rule". Type ----------------------------------------- -------- ---------------------------- BIN VARCHAR2(10) ACT_SL VARCHAR2(3) ACT_CODE VARCHAR2(11) ACT_VAL NUMBER(14,2) ENTRY_DATE DATE SQL DESC VIS. Type ----------------------------------------- -------- ---------------------------- BIN VARCHAR2(10) ACT_SL VARCHAR2(3) ACT_CODE VARCHAR2(11) ACT_VAL NUMBER(14,2) ENTRY_DATE DATE SQL UPDATE (SELECT DBHSCODELIST. the database needs to know that each row in dbhscodelist will map to AT MOST one row in hscodelist - this mandates a primary or unqiue key constraint on the join columns this is discussed in the original answer above.

But I am trapped by the method that without using cursor to achieve it. I have another table B containg 10,000 records of incremented and edited records of A table. I am using the following codes to append data from B to A. Normally, I would try to use a single sql statment -- here, due to the "data being spread all over the place", and being distributed and all. We have a 2 CPU machine where at normal times, the topmost entry in top command shows only .2 or .3 percentage of CPU use. This is on a test database where nothing else is going on concurrently.

When an ISAM file is created, index nodes are fixed, and their pointers do not change during inserts and deletes that occur later (only content of leaf nodes change afterwards).

As a consequence of this, if inserts to some leaf node exceed the node's capacity, new records are stored in overflow chains.

Leave a Reply