Performance issue in update query

Question

I have one small doubt in query performance. Basically, I have a table with more than 1C records. sl_id is the primary key in that table. Currently, I am updating the table column status to true (default false) by using the sl_id.

In my program, I will have 200 unique sl_id in an array. I am updating the status to true(always) by using each sl_id.

My doubt:

Shall I use individual update queries by specifing each sl_id in a where condition to update the status?

(OR)

Shall I use IN operator and put all 200 unique sl_id in one single query?

Which one will be faster?

Answer 1

In rough order of slower to faster:

• 200 Individual queries, each in their own transaction
• 200 Individual queries, all in one transaction
• 1 big query with WHERE ... IN (...) or WHERE EXISTS (SELECT ...)
• 1 big query with an INNER JOIN over a VALUES clause
• (only faster for very big lists of values): COPY value list to a temp table, index it, and JOIN on the temp table.

If you're using hundreds of values I really suggest joining over a VALUES clause. For many thousands of values, COPY to a temp table and index it then join on it.

An example of joining on a values clause. Given this IN query:

SELECT *
FROM mytable
WHERE somevalue IN (1, 2, 3, 4, 5);

the equivalent with VALUES is:

SELECT *
FROM mytable
INNER JOIN (
  VALUES (1), (2), (3), (4), (5)
) vals(v)
ON (somevalue = v);

Note, however, that using VALUES this way is a PostgreSQL extension, wheras IN, or using a temporary table, is SQL standard.

See this related question:

• Postgres NOT IN performance

Answer 2

Definitely you should use WHERE IN operator. Making 200 queries is much slower than one bigger. Remember, when you sending query to database, there is additional time needed to communicate between server and DB and this will crush your performance.

Answer 3

Definitely IN is more powerful, but again the number of match to check in IN will make performance issue.

So, I will suggest to use IN but with BATCH, as in if you have 200 record to update then part in 50 each and then make 4 UPDATE query, or something like that.

Hope it helps...!!