Updating millions of rows
Updating millions of rows - No sign up dirty live chat
Update-wise, it looks as though it should perform the same as the Explicit Cursor Loop.
Note that, even if you create a new table with the same name the requests will still fail because they use the table OID.This article contains some of the things we learned while dealing with these problems.The Postgres documentation and some stack exchange answers have more in depth information about some topics mentioned here and are worth checking if you need more details.I include it here because it allows us to compare the cost of context-switches to the cost of updates.DECLARE CURSOR c1 IS SELECT * FROM test6; rec_cur c1%rowtype; BEGIN OPEN c1; LOOP FETCH c1 INTO rec_cur; EXIT WHEN c1%notfound; UPDATE test SET fk = rec_, fill = rec_WHERE pk = rec_cur.pk; END LOOP; CLOSE C1; END; / This is the simplest PL/SQL method and very common in hand-coded PL/SQL applications.To support this method, I needed to create an index on TEST8. The biggest drawback to this method is readability. LAST UPDATE test SET fk = fk_tab(i) , fill = fill_tab(i) WHERE pk = pk_tab(i); END LOOP; CLOSE rec_cur; END; / The modern equivalent of the Updateable Join View. Parallel PL/SQL ORA-00060: deadlock detected Well, if further proof was needed that Bitmap indexes are inappropriate for tables that are maintained by multiple concurrent sessions, surely this is it.
Since Oracle does not yet provide support for record collections in FORALL, we need to use scalar collections, making for long declarations, INTO clauses, and SET clauses. Gaining in popularity due to its combination of brevity and performance, it is primarily used to INSERT and UPDATE in a single statement. Note that I have included a FIRST_ROWS hint to force an indexed nested loops plan. The Deadlock error raised by Method 8 occurred because bitmap indexes are locked at the block-level, not the row level.
Besides this, here is a list of things that you should know when you need to update large tables: With this in mind, let’s look at a few strategies that you can use to effectively update a large number of rows in your table: If you can segment your data using, for example, sequential IDs, you can update rows incrementally in batches.
This maximizes your table availability since you only need to keep locks for a short amount of time.
In case of adding a new column, you can set it temporarily as nullable and start gradually filling it with new values.
The main problem with this approach is the performance, it is a very slow process because in place updates are costly.
I spend an inordinate proportion of design time of an ETL system worrying about the relative proportion of rows inserted vs updated.