Is count (*) faster than count 1?
I can't speak for all database engines, but in Oracle this was a common myth that count(1) is faster than count(*). It is not true; they're equivalent.Count(*): It will get the data of all rows without any processing, and add 1 to the number of rows. Count(1): It will get the data of all rows, each row has a fixed value of 1, which also add 1 to the number of rows.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.

What is the difference between count (*) count 1 count 0 : Since both 0 and 1 are non-null values, COUNT(0)=COUNT(1) and they both will be equivalent to the number of rows COUNT(*) . It's a different concept, but the result will be the same. Now – they should all perform identically.

Is count (*) slower than count 1

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.

Is count (*) and count 1 same : The COUNT(*)returns the total number of rows in a table, including the NULLs. My Emp table returns 5 that shows the total records in that table. The COUNT(1) function replaces all records from the query result set with value 1. If you have NULL values, it is also replaced by 1.

Using the COUNT(*) function with large tables can be inefficient, as it requires the database to count all rows in the table.

In conclusion, the myth that SELECT COUNT(*) is slower, and that specifying a column name can lead to better performance, has been debunked. In practice, both approaches lead to the same efficient utilization of indexes by database systems like MySQL.

How fast is count in SQL

Reference of Techniques

Technique Time/1M rows Exact
count(*) 85ms ✅
count(1) 99ms ✅
Idx Only Scan 177ms ✅
HLL 239ms ❌

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. I_S.By default, COUNT(*) traverses the smallest available secondary index to calculate the count. Not the primary key! The optimizer assumes that it's going to have to read the entire index from disk to get the count.

Reference of Techniques

Technique Time/1M rows Exact
count(*) 85ms ✅
count(1) 99ms ✅
Idx Only Scan 177ms ✅
HLL 239ms ❌

Which is faster count (*) or count column : Developers often think that COUNT(column_name) is faster then COUNT(*) , because COUNT(*) would have to read all columns of each row), while COUNT(column_name) only need to read the specified column. This is only sometimes correct though, in many cases, it is even entirely the opposite.

Is count (*) slower : TL;DR: COUNT(*) is optimized to be fast, you should use it. You have probably read in a bunch of different places that you shouldn't use SELECT(*) in MySQL when you don't need all the data. SELECT(*) selects all the columns in the table, not just the ones that you might need. This is generally good advice!