SELECT
s and Other Queries
EXPLAIN
Syntax (Get Information About a SELECT
)
SELECT
Queries
WHERE
Clauses
IS NULL
DISTINCT
LEFT JOIN
and RIGHT JOIN
ORDER BY
LIMIT
INSERT
Queries
UPDATE
Queries
DELETE
Queries
In most cases you can estimate the performance by counting disk seeks.
For small tables, you can usually find the row in 1 disk seek (as the
index is probably cached). For bigger tables, you can estimate that
(using B++ tree indexes) you will need: log(row_count) /
log(index_block_length / 3 * 2 / (index_length + data_pointer_length)) +
1
seeks to find a row.
In MySQL an index block is usually 1024 bytes and the data
pointer is usually 4 bytes. A 500,000 row table with an
index length of 3 (medium integer) gives you:
log(500,000)/log(1024/3*2/(3+4)) + 1
= 4 seeks.
As the above index would require about 500,000 * 7 * 3/2 = 5.2M, (assuming that the index buffers are filled to 2/3, which is typical) you will probably have much of the index in memory and you will probably only need 1-2 calls to read data from the OS to find the row.
For writes, however, you will need 4 seek requests (as above) to find where to place the new index and normally 2 seeks to update the index and write the row.
Note that the above doesn't mean that your application will slowly degenerate by log N! As long as everything is cached by the OS or SQL server things will only go marginally slower while the table gets bigger. After the data gets too big to be cached, things will start to go much slower until your applications is only bound by disk-seeks (which increase by log N). To avoid this, increase the index cache as the data grows. See section 5.5.2 Tuning Server Parameters.