Bulk updating multiple columns in oracle
The result is (same time unit): Run 0, Statement 1: PT4.546S Run 0, Statement 2: PT3.52S Run 0, Statement 3: PT0.144S Run 0, Statement 4: PT0.028S Run 1, Statement 1: PT3.712S Run 1, Statement 2: PT3.185S Run 1, Statement 3: PT0.138S Run 1, Statement 4: PT0.025S Run 2, Statement 1: PT3.481S Run 2, Statement 2: PT3.007S Run 2, Statement 3: PT0.122S Run 2, Statement 4: PT0.026S Run 3, Statement 1: PT3.518S Run 3, Statement 2: PT3.077S Run 3, Statement 3: PT0.113S Run 3, Statement 4: PT0.027S Run 4, Statement 1: PT3.54S Run 4, Statement 2: PT2.94S Run 4, Statement 3: PT0.123S Run 4, Statement 4: PT0.03S The difference between Statement 1 and 4 is a factor of 100x !! Again (by far): Statement 4, running the bulk update In fact, the time is not too far away from the time taken by PL/SQL.
With larger data sets being updated, the two results will converge. While we're running individual row-by-row updates, we're sending all the update statements in one batch to the SQL engine.
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So, remember: stop doing row-by-row (slow-by-slow) operations when you could run the same operation in bulk, in a single SQL statement.
Hint: Always know what your ORM (if you're using one) is doing, because the ORM can help you with automatic batching/bulking in many cases.
) Or, by emulating anonymous blocks using the JDBC batch API (has its limitations), or by writing stored procedures.
The performance gain is not always worth the trouble of moving logic from the client to the server, but very often (as in the above case), the move is a no-brainer and there's absolutely no reason against it.
The traditional advice for improving performance for multiple UPDATE statements is to “prepare” the required query once, and then “execute” the prepared query once for each row requiring an update.
The terms "snapshot" and "materialized view" are synonymous.
table_collection_expression can be a subquery, a column, a function, or a collection constructor, it must return a collection value (that is, a value whose type is nested table or varray).
Something that has been said many times, but needs constant repeating until every developer is aware of its importance, is the performance difference between row-by-row updating and bulk updating. Let me ask this differently: The best way to find out is to benchmark.
If you cannot guess which one will be much faster, remember that row-by-row kinda rhymes with "slow-by-slow" (hint, hint). I'm doing two benchmarks for this: Run 1, Statement 1 : .01457 (avg : .0098) Run 1, Statement 2 : .0133 (avg : .01291) Run 1, Statement 3 : .02351 (avg : .02519) Run 2, Statement 1 : .00882 (avg : .0098) Run 2, Statement 2 : .01159 (avg : .01291) Run 2, Statement 3 : .02348 (avg : .02519) Run 3, Statement 1 : .01012 (avg : .0098) Run 3, Statement 2 : .01453 (avg : .01291) Run 3, Statement 3 : .02544 (avg : .02519) Run 4, Statement 1 : .00799 (avg : .0098) Run 4, Statement 2 : .01346 (avg : .01291) Run 4, Statement 3 : .02958 (avg : .02519) Run 5, Statement 1 : .00749 (avg : .0098) Run 5, Statement 2 : .01166 (avg : .01291) Run 5, Statement 3 : .02396 (avg : .02519) The difference between Statement 1 and 3 is a factor of 2.5x.Let's say we have a simple table for our blog posts (using Oracle syntax, but the effect is the same on all databases): CREATE TABLE post ( id INT NOT NULL PRIMARY KEY, text VARCHAR2(1000) NOT NULL, archived NUMBER(1) NOT NULL CHECK (archived IN (0, 1)), creation_date DATE NOT NULL ); CREATE INDEX post_creation_date_i ON post (creation_date); statement is probably better than looping over each individual row and updating each individual row explicitly. Showing the time it takes for each statement type to complete, each time updating 3649 / 10000 rows.