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?
Best practices to improve SQL update statement performance We need to consider the lock escalation mode of the modified table to minimize the usage of too many resources. Analyzing the execution plan may help to resolve performance bottlenecks of the update query. We can remove the redundant indexes on the table.
Steps to take to improve performance of queries: - Create all primary and foreign keys and relationships among tables. - Avoid using Select*, rather mention the needed columns and narrow the resultset as needed. - Implement queries as stored procedures. - Have a WHERE Clause in all SELECT queries.
In rough order of slower to faster:
WHERE ... IN (...)
or WHERE EXISTS (SELECT ...)
INNER JOIN
over a VALUES
clauseCOPY
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:
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.
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