My SQL Interview Questions and Answers

How MySQL Uses Indexes ?
Indexes are used to find rows with a specific value of one column fast. Without an index MySQL has to start with the first record and then read through the whole table until it finds the relevant rows. The bigger the table, the more this costs. If the table has an index for the colums in question, MySQL can quickly get a position to seek to in the middle of the data file without having to look at all the data. If a table has 1000 rows, this is at least 100 times faster than reading sequentially. Note that if you need to access almost all 1000 rows it is faster to read sequentially because we then avoid disk seeks.

All MySQL indexes (PRIMARY, UNIQUE, and INDEX) are stored in B-trees. Strings are automatically prefix- and end-space compressed.

Indexes are used to:

Quickly find the rows that match a WHERE clause.
Retrieve rows from other tables when performing joins.
Find the MAX() or MIN() value for a specific indexed column. This is optimized by a preprocessor that checks if you are using WHERE key_part_# = constant on all key parts < N. In this case MySQL will do a single key lookup and replace the MIN() expression with a constant. If all expressions are replaced with constants, the query will return at once:
SELECT MIN(key_part2),MAX(key_part2) FROM table_name where key_part1=10

Sort or group a table if the sorting or grouping is done on a leftmost prefix of a usable key (for example, ORDER BY key_part_1,key_part_2 ). The key is read in reverse order if all key parts are followed by DESC. The index can also be used even if the ORDER BY doesn't match the index exactly, as long as all the unused index parts and all the extra are ORDER BY columns are constants in the WHERE clause. The following queries will use the index to resolve the ORDER BY part:
SELECT * FROM foo ORDER BY key_part1,key_part2,key_part3;
SELECT * FROM foo WHERE column=constant ORDER BY column, key_part1;
SELECT * FROM foo WHERE key_part1=const GROUP BY key_part2;

In some cases a query can be optimized to retrieve values without consulting the data file. If all used columns for some table are numeric and form a leftmost prefix for some key, the values may be retrieved from the index tree for greater speed:
SELECT key_part3 FROM table_name WHERE key_part1=1

Suppose you issue the following SELECT statement:

mysql> SELECT * FROM tbl_name WHERE col1=val1 AND col2=val2;

If a multiple-column index exists on col1 and col2, the appropriate rows can be fetched directly. If separate single-column indexes exist on col1 and col2, the optimizer tries to find the most restrictive index by deciding which index will find fewer rows and using that index to fetch the rows.

If the table has a multiple-column index, any leftmost prefix of the index can be used by the optimizer to find rows. For example, if you have a three-column index on (col1,col2,col3), you have indexed search capabilities on (col1), (col1,col2), and (col1,col2,col3).

MySQL can't use a partial index if the columns don't form a leftmost prefix of the index. Suppose you have the SELECT statements shown below:

mysql> SELECT * FROM tbl_name WHERE col1=val1;
mysql> SELECT * FROM tbl_name WHERE col2=val2;
mysql> SELECT * FROM tbl_name WHERE col2=val2 AND col3=val3;

If an index exists on (col1,col2,col3), only the first query shown above uses the index. The second and third queries do involve indexed columns, but (col2) and (col2,col3) are not leftmost prefixes of (col1,col2,col3).
MySQL also uses indexes for LIKE comparisons if the argument to LIKE is a constant string that doesn't start with a wild-card character. For example, the following SELECT statements use indexes:
mysql> select * from tbl_name where key_col LIKE "Patrick%";
mysql> select * from tbl_name where key_col LIKE "Pat%_ck%";

In the first statement, only rows with "Patrick" <= key_col < "Patricl" are considered. In the second statement, only rows with "Pat" <= key_col < "Pau" are considered.

The following SELECT statements will not use indexes:

mysql> select * from tbl_name where key_col LIKE "%Patrick%";
mysql> select * from tbl_name where key_col LIKE other_col;

In the first statement, the LIKE value begins with a wild-card character. In the second statement, the LIKE value is not a constant.

Searching using column_name IS NULL will use indexes if column_name is an index.

MySQL normally uses the index that finds the least number of rows. An index is used for columns that you compare with the following operators: =, >, >=, >, >=, BETWEEN, and a LIKE with a non-wild-card prefix like 'something%'.

Any index that doesn't span all AND levels in the WHERE clause is not used to optimize the query. In other words: To be able to use an index, a prefix of the index must be used in every AND group.

The following WHERE clauses use indexes:

... WHERE index_part1=1 AND index_part2=2 AND other_column=3
... WHERE index=1 OR A=10 AND index=2 /* index = 1 OR index = 2 */
... WHERE index_part1='hello' AND index_part_3=5
/* optimized like "index_part1='hello'" */
... WHERE index1=1 and index2=2 or index1=3 and index3=3;
/* Can use index on index1 but not on index2 or index 3 */

These WHERE clauses do NOT use indexes:

... WHERE index_part2=1 AND index_part3=2 /* index_part_1 is not used */
... WHERE index=1 OR A=10 /* Index is not used in both AND parts */
... WHERE index_part1=1 OR index_part2=10 /* No index spans all rows */

Note that in some cases MySQL will not use an index, even if one would be available. Some of the cases where this happens are:

If the use of the index would require MySQL to access more than 30 % of the rows in the table. (In this case a table scan is probably much faster, as this will require us to do much fewer seeks). Note that if such a query uses LIMIT to only retrieve part of the rows, MySQL will use an index anyway, as it can much more quickly find the few rows to return in the result.

MySQL - Speed of Queries that Access or Update Data
First, one thing that affects all queries: The more complex permission system setup you have, the more overhead you get.

If you do not have any GRANT statements done, MySQL will optimize the permission checking somewhat. So if you have a very high volume it may be worth the time to avoid grants. Otherwise more permission check results in a larger overhead.

If your problem is with some explicit MySQL function, you can always time this in the MySQL client:

mysql> select benchmark(1000000,1+1);
+------------------------+
| benchmark(1000000,1+1) |
+------------------------+
| 0 |
+------------------------+
1 row in set (0.32 sec)

The above shows that MySQL can execute 1,000,000 + expressions in 0.32 seconds on a PentiumII 400MHz.

All MySQL functions should be very optimized, but there may be some exceptions, and the benchmark(loop_count,expression) is a great tool to find out if this is a problem with your query.

MySQL - Estimating Query Performance

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 N 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 N log N). To avoid this, increase the index cache as the data grows.

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