Frequently, a full table
scan is a danger sign that a query can be speeded up
significantly. For tables with more than a few rows, consider
redesigning the query by adding an index for one or more of
the columns tested in the
WHERE clause. Put
extra effort into avoiding table scans for queries that
perform joins or reference foreign keys. If the nature of the
data means there is no way to avoid reading all the rows, then
it might not be practical to make the query faster, or making
it faster might involve extensive restructuring of your tables
that is beyond the scope of this section.
The output from
ALL in the
type column when MySQL uses a table scan to
resolve a query. This usually happens under the following
clauses do not reference any indexed columns that the
query can use. Consider adding an index, or refining those
clauses to refer to an indexed column.
The table is so small that it is faster to perform a table scan than to bother with a key lookup. This is common for tables with fewer than 10 rows and a short row length. Don't worry in this case.
You are comparing indexed columns with constant values and
MySQL has calculated (based on the index tree) that the
constants cover too large a part of the table and that a
table scan would be faster. See
Section 188.8.131.52, “How MySQL Optimizes
WHERE Clauses”. For example, to
query census data only for males or only for females,
MySQL must read most of the data blocks in the table, so
locating the rows through the index would add unnecessary
overhead. Don't worry if you encounter this condition for
occasional big reports. If these reports are frequent or
truly time-critical, and the table is huge, you might
partition, shard, or create dimension tables using the
You are using a key with low cardinality (many rows match the key value) through another column. In this case, MySQL assumes that by using the key it probably will do many key lookups and that a table scan would be faster.
For small tables, a table scan often is appropriate and the performance impact is negligible. For large tables, try the following techniques to avoid having the optimizer incorrectly choose a table scan:
OR keywords in your
WHERE clauses. If there is no index
that helps to locate the values on
both sides of the
OR, any row could potentially be part
of the result set, so all rows must be tested, and that
requires a full table scan. If you have one index that
helps to optimize one side of an
query, and a different index that helps to optimize the
other side, use a
UNION operator to run
separate fast queries and merge the results afterward.
With tables that use the
engine, if you run queries that examine ranges of values
(using operators such as
the indexed columns), create the index with the
USING BTREE clause. The default
USING HASH) is fast for retrieving
individual rows with an equality operator
but is much slower (requiring a full table scan) to
examine a range of column values. A
MEMORY table created with the
USING BTREE clause is still fast for
equality comparisons, so use that clause for your
MEMORY tables that handle a variety of
the key distributions for the scanned table. See
Section 184.108.40.206, “
ANALYZE TABLE Syntax”.
FORCE INDEX for the scanned table
to tell MySQL that table scans are very expensive compared
to using the given index:
SELECT * FROM t1, t2 FORCE INDEX (
index_for_column) WHERE t1.
Start mysqld with the
option or use
max_seeks_for_key=1000 to tell the optimizer to
assume that no key scan causes more than 1,000 key seeks.
See Section 5.1.4, “Server System Variables”.
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