How do you make select count faster?
So to make SELECT COUNT(*) queries fast, here's what to do:
Get on any version that supports batch mode on columnstore indexes, and put a columnstore index on the table – although your experiences are going to vary dramatically depending on the kind of query you have.
Because all data (including Row Data) is stored in B-Tree indexes, performing a select count(PK_COLUMN) is still a considerable amount of IO (needs to reads all data pages). If you have a secondary index on the PK field, it will be able to perform less IO to perform a count.
Less data needs to be transferred and processed.
Your COUNT query will only ever result in one row with one value so this will be quick, the other query could result in a great many rows being transferred to, and processed by, the application.
The simple answer is no – there is no difference at all. The COUNT(*) function counts the total rows in the table, including the NULL values. The semantics for COUNT(1) differ slightly; we'll discuss them later. However, the results for COUNT(*) and COUNT(1) are identical.
Sometimes COUNT(1) was faster, sometimes COUNT(*) was faster, so all differences were only benchmark artifacts. Oracle: Doesn't matter. Like MySQL.
- 1) Clarify Your Information Needs:
- 2) Check the WHERE Clause:
- 3) Check the GROUP BY and ORDER BY Clauses:
- 4) Zoom Out to Consider All Data Requests:
- 5) Merge Indexes and Delete the Ones You Don't Need:
- 6) Define Your Asterisk!
INTO' creates the destination table, it exclusively owns that table and is quicker compared to the 'INSERT … SELECT'. Because the 'INSERT … SELECT' inserts data into an existing table, it is slower and requires more resources due to the higher number of logical reads and greater transaction log usage.
- Tip 1: Optimize Queries. ...
- Tip 2: Improve Indexes. ...
- Tip 3: Defragment Data. ...
- Tip 4: Increase Memory. ...
- Tip 5: Strengthen CPU. ...
- Tip 6: Review Access. ...
- SolarWinds Database Performance Analyzer (DPA) ...
- SolarWinds Database Performance Monitor (DPM)
- Colourful abacus. Purchase an abacus with large, colourful beads. ...
- Magnetic numbers. At first, use several "1's", "2's", and "3's". ...
- Bouncing balls. ...
- A tower to build, blocks to count. ...
- Drinking straw pieces. ...
- Cut and re-cut cheese. ...
- Adhesive numbers. ...
- One line, two lines, many lines!
- COUNTA: To count cells that are not empty.
- COUNT: To count cells that contain numbers.
- COUNTBLANK: To count cells that are blank.
- COUNTIF: To count cells that meets a specified criteria.
How long does it take to count from 1 to 1000?
So ... how long does it take to count to 1,000? Answer: 10 times 25 seconds, and then double that because it takes longer to say "one-hundred etc...", so that is about 10 minutes.
Across four studies we find that (a) counting downward results in shorter time estimates and more favorable attitudes towards the counting task than counting upward, (b) the effect is stronger for individuals who are high in need-for-cognition, and (c) the effect is driven by one's implicit goal.

The record for being the fastest to recite counting from 1 to 100 was set by Mihit Bhandari (born on May 22, 2018) of Haldwani, Uttarakhand. He fluently recited the counting from 1 to 100 in just 1 minute, 7 seconds and 90 milliseconds, at the age of 3 years and 4 days, as confirmed on May 26, 2021.
COUNT(*) returns the number of rows in a specified table, and it preserves duplicate rows. It counts each row separately. This includes rows that contain null values.
We can also skip count on the number line by simply jumping by a particular number to make a sequence. As an example, if we were to skip count by 2, starting at 0, the next number will be 0 + 2 = 2 , then, 2 + 2 = 4 , then 4 + 2 = 6 , then 6 + 2 = 8 , and then, 10, 12, 14, 16, 18 and so on.
Pick up one stack and hold it in your nondominant hand. Grab the top bill with your dominant thumb and pointer finger to make sure it's only 1 bill. Then place the bill down on a flat surface in front of you while you count it. Continue to grab and move the bills one at a time while counting by the denomination.
Query optimization is a process of defining the most efficient and optimal way and techniques that can be used to improve query performance based on rational use of system resources and performance metrics.
SELECT ... or SELECT ... INTO it takes up to 30 seconds. The table is more like a data copy of a view, for performance reasons which gets truncated and filled with the data from time to time.
there is no difference. A view is just a stored query which can be referred to in sql queries as though they are tables. Note that this does not apply to materialized views. A view is only a query stored in the data dictionary: it is not going to make your query run faster or slower.
The select query runs much faster than a similar delete query. Delete does more work than select. But how many rows are found, and how many rows are deleted? (Which should be the same number, of course).
How do I make SQL Select query faster?
The way to make a query run faster is to reduce the number of calculations that the software (and therefore hardware) must perform. To do this, you'll need some understanding of how SQL actually makes calculations.
- Define business requirements first. ...
- SELECT fields instead of using SELECT * ...
- Avoid SELECT DISTINCT. ...
- Create joins with INNER JOIN (not WHERE) ...
- Use WHERE instead of HAVING to define filters. ...
- Use wildcards at the end of a phrase only. ...
- Use LIMIT to sample query results.
Question: Is there a way to optimizer a SQL count(*) operation? Answer: Without a where clause, a count(*) will always perform a full-table scan, and the only way to improve the performance of the count(*) is to use Oracle parallel query.
- Avoid using functions in predicates. ...
- Avoid using a wildcard (%) at the beginning of a predicate. ...
- Avoid unnecessary columns in SELECT clause. ...
- Use inner join, instead of outer join if possible. ...
- Use DISTINCT and UNION only if it is necessary.
Queries can become slow for various reasons ranging from improper index usage to bugs in the storage engine itself. However, in most cases, queries become slow because developers or MySQL database administrators neglect to monitor them and keep an eye on their performance.
WAITING: Queries can be slow because they're waiting on a bottleneck for a long time. See a detailed list of bottlenecks in types of Waits. RUNNING: Queries can be slow because they're running (executing) for a long time. In other words, these queries are actively using CPU resources.
Create SQL Server Indexes
You can retrieve data faster and optimize SQL queries by using clustered and non-clustered SQL Server indexes. Indexes can reduce runtime, but it's also important to consider how much disk space they require.
- 1) Clarify Your Information Needs:
- 2) Check the WHERE Clause:
- 3) Check the GROUP BY and ORDER BY Clauses:
- 4) Zoom Out to Consider All Data Requests:
- 5) Merge Indexes and Delete the Ones You Don't Need:
- 6) Define Your Asterisk!
To be exact, COUNT(column) counts rows in which values of column is not NULL, whereas COUNT(*) counts all rows of the table.
Count will do either a table scan or an index scan. So for a high number of rows it will be slow. If you do this operation frequently, the best way is to keep the count record in another table. I am suggesting creating a new index, because this one (if it will not be used) will not get locked during other operations.
How do I tune a performance in Oracle?
- Tip 1 – Monitor Wait Times. Oracle provides wait events that allow you to understand the total time a query spends in the database. ...
- Tip 2 – Review the Execution Plan. ...
- Tip 3 – Gather Object Information. ...
- Tip 4 – Find the Driving Table. ...
- Tip 5 – Identify Performance Inhibitors.
INTO' creates the destination table, it exclusively owns that table and is quicker compared to the 'INSERT … SELECT'. Because the 'INSERT … SELECT' inserts data into an existing table, it is slower and requires more resources due to the higher number of logical reads and greater transaction log usage.
The answer, in short, is yes. If you limit your result to 1, then even if you are "expecting" one result, the query will be faster because your database wont look through all your records. It will simply stop once it finds a record that matches your query.