If I execute this query:
SELECT * FROM table1 WHERE name LIKE '%girl%'
It returns all records where name contains 'girl'. However, because of the first wildcard % in the LIKE statment, it cannot (or does not) use indexes as stated here: Mysql Improve Search Performance with wildcards (%%)
Then I changed the query to:
SELECT * FROM table1 WHERE name LIKE 'girl%' OR name LIKE '%girl%'
On the leftside of the OR I removed the wildcard so it can use indexes. But the performance win depends on how MySQL evaluates the query.
Hence my question: Does the performance of my query increases when I add the OR statement?
No, the performance will be the same. MySQL still has to evaluate the first condition (LIKE '%girl%') because of the OR. Then it can evaluate the second condition using index. You can see this info when you EXPLAIN your query (mysql will show that it stills needs to do a full table scan, which means check each row):
EXPLAIN SELECT * FROM table1 WHERE name LIKE 'girl%' OR name LIKE '%girl%'
For better performance for these kinds of queries you would need to use Fulltext indexes and special syntax for querying them. But FT indexes behave different and are not suited for everything.
(This answer provides a summary of the comments, plus contradicts some of the previous notes.)
Leading wildcard:
SELECT * FROM table1 WHERE name LIKE 'girl%' OR name LIKE '%girl%'
SELECT * FROM table1 WHERE name LIKE '%girl%'
Either of those will do a table scan and ignore any indexes. This both because of the leading wild card and the OR. (It will not use the index for 'girl%', contrary to what #Marki555 says -- it's not worth the extra effort.)
Range query via LIKE (no leading wildcard):
SELECT * FROM table1 WHERE name LIKE 'girl%'
will probably use INDEX(name) in the following way:
Drill down the BTree for that index to the first name starting with "girl";
Scan forward (in the index) until the last row starting with "girl";
For each item in step 2, reach over into the data to get *.
Since Step 3 can be costly, the optimizer first estimates how many rows will need to be touched in Step 2. If more than 20% (approx) of the table, it will revert to a table scan. (Hence, my use of "probably".)
"Covering index":
SELECT name FROM table1 WHERE name LIKE '%girl%'
This will always use INDEX(name). That is because the index "covers". That is, all the columns in the SELECT are found in the INDEX. Since an INDEX looks and feels like a table, scanning the index is the best way to do the query. Since an index is usually smaller than the table, an index scan is usually faster than a table scan.
Here's a less obvious "covering index", but it applies only to InnoDB:
PRIMARY KEY(id)
INDEX(name)
SELECT id FROM table1 WHERE name LIKE '%girl%'
Every secondary key (name) in InnoDB implicitly includes the PK (id). Hence the index looks like (name, id). Hence all the columns in the SELECT are in the index. Hence it is a "covering index". Hence it will use the index and do an "index scan".
A "covering index" is indicated by Using index showing up in the EXPLAIN SELECT ....
Related
I have a table with 150k rows of data, and I have column with a UNIQUE INDEX, It has a type of VARCHAR(10) and stores 10 digit account numbers.
Now whenever I query, like a simple one:
SELECT * FROM table WHERE account_number LIKE '0103%'
It results 30,000+ ROWS, and when I run a EXPLAIN on my query It shows no INDEX is used.
But when I do:
SELECT * FROM table WHERE account_number LIKE '0104%'
It results 4,000+ ROWS, with the INDEX used.
Anyone can explain this?
I'm using MySQL 5.7 Percona XtraDB.
30k+/150k > 20% and I guess it is faster to do table scan. From 8.2.1.19 Avoiding Full Table Scans:
The output from EXPLAIN shows ALL in the type column when MySQL uses a full table scan to resolve a query. This usually happens under the following conditions:
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.
If you don't need all values try to use:
SELECT account_number FROM table WHERE account_number LIKE '0103%'
instead of SELECT *. Then your index will become covering index and optimizer should always use it (as long as WHERE condition is SARGable).
The most database uses B tree for indexing. In this case the database optimizer don't use the index because its faster to scan without index. Like #lad2025 explained.
Your database column is unique and i think your cardinality of your index is high. But since your query using the like filter the database optimizer decides for you to choose not to use the index.
You can use try force index to see the result. Your using varchar with unique index. I would choose another data type or change your index type. If your table only contains numbers change it to numbers. This will help to optimize you query a lot.
In some cases when you have to use like you can use full text index.
If you need help with optimizing your query and table. Provide us more info and which info you want to fetch from your table.
lad2025 is correct. The database is attempting to make an intelligent optimization.
Benchmark with:
SELECT * FROM table FORCE INDEX(table_index) WHERE account_number LIKE '0103%'
and see who is smarter :-) You can always try your hand at questioning the optimizer. That's what index hints are for...
https://dev.mysql.com/doc/refman/5.7/en/index-hints.html
I have a table with two fields: a,b
Both fields are indexed separately -- no compound index.
While trying to run a select query with both fields:
select * from table where a=<sth> and b=<sth>
It took over 400ms. while
select * from table where a=<sth>
took only 30ms;
Do I need set a compound index for (a,b)?
Reasonably, if I have indexes on both a and b, it should be fast for queries of a AND b like above right?
For this query:
select *
from table
where a = <sth> and b = <sth>;
The best index is on table(a, b). This can also be used for your second query as well.
Usually (but not always).
In your case the number of different values in a (and b) and the number of columns you use in your select can change the way db decide to use index / table.
For example,
if in table you have,say, 100.000 records and 80.000 of them have the same value for a, when you query for:
SELECT * FROM table WHERE a=<your value>
db engine could decide to "scan" directly the table without using the index, while if you query
SELECT a, b FROM table WHERE a=<your value>
and in index you added column b too (in index directly or with INCLUDE) it's quite probable that db engine will use the index.
Try to give a look on internet for index tips and give a look too to How can I index these queries?
The SQLite documentation explains how index lookups work.
Once the database has used an index to look up some rows, the other index is no longer efficient to use (there is no easy method to filter the results of the first lookup because the other index refers to rows in the original table, not to entries in the first index). See Multiple AND-Connected WHERE-Clause Terms.
To make index lookups on two columns as fast as possible, you need Multi-Column Indices.
I've just heard the term covered index in some database discussion - what does it mean?
A covering index is an index that contains all of, and possibly more, the columns you need for your query.
For instance, this:
SELECT *
FROM tablename
WHERE criteria
will typically use indexes to speed up the resolution of which rows to retrieve using criteria, but then it will go to the full table to retrieve the rows.
However, if the index contained the columns column1, column2 and column3, then this sql:
SELECT column1, column2
FROM tablename
WHERE criteria
and, provided that particular index could be used to speed up the resolution of which rows to retrieve, the index already contains the values of the columns you're interested in, so it won't have to go to the table to retrieve the rows, but can produce the results directly from the index.
This can also be used if you see that a typical query uses 1-2 columns to resolve which rows, and then typically adds another 1-2 columns, it could be beneficial to append those extra columns (if they're the same all over) to the index, so that the query processor can get everything from the index itself.
Here's an article: Index Covering Boosts SQL Server Query Performance on the subject.
Covering index is just an ordinary index. It's called "covering" if it can satisfy query without necessity to analyze data.
example:
CREATE TABLE MyTable
(
ID INT IDENTITY PRIMARY KEY,
Foo INT
)
CREATE NONCLUSTERED INDEX index1 ON MyTable(ID, Foo)
SELECT ID, Foo FROM MyTable -- All requested data are covered by index
This is one of the fastest methods to retrieve data from SQL server.
Covering indexes are indexes which "cover" all columns needed from a specific table, removing the need to access the physical table at all for a given query/ operation.
Since the index contains the desired columns (or a superset of them), table access can be replaced with an index lookup or scan -- which is generally much faster.
Columns to cover:
parameterized or static conditions; columns restricted by a parameterized or constant condition.
join columns; columns dynamically used for joining
selected columns; to answer selected values.
While covering indexes can often provide good benefit for retrieval, they do add somewhat to insert/ update overhead; due to the need to write extra or larger index rows on every update.
Covering indexes for Joined Queries
Covering indexes are probably most valuable as a performance technique for joined queries. This is because joined queries are more costly & more likely then single-table retrievals to suffer high cost performance problems.
in a joined query, covering indexes should be considered per-table.
each 'covering index' removes a physical table access from the plan & replaces it with index-only access.
investigate the plan costs & experiment with which tables are most worthwhile to replace by a covering index.
by this means, the multiplicative cost of large join plans can be significantly reduced.
For example:
select oi.title, c.name, c.address
from porderitem poi
join porder po on po.id = poi.fk_order
join customer c on c.id = po.fk_customer
where po.orderdate > ? and po.status = 'SHIPPING';
create index porder_custitem on porder (orderdate, id, status, fk_customer);
See:
http://literatejava.com/sql/covering-indexes-query-optimization/
Lets say you have a simple table with the below columns, you have only indexed Id here:
Id (Int), Telephone_Number (Int), Name (VARCHAR), Address (VARCHAR)
Imagine you have to run the below query and check whether its using index, and whether performing efficiently without I/O calls or not. Remember, you have only created an index on Id.
SELECT Id FROM mytable WHERE Telephone_Number = '55442233';
When you check for performance on this query you will be dissappointed, since Telephone_Number is not indexed this needs to fetch rows from table using I/O calls. So, this is not a covering indexed since there is some column in query which is not indexed, which leads to frequent I/O calls.
To make it a covered index you need to create a composite index on (Id, Telephone_Number).
For more details, please refer to this blog:
https://www.percona.com/blog/2006/11/23/covering-index-and-prefix-indexes/
I have an index structured as so:
BTREE merchant_id
BTREE flag
BTREE test (merchant_id, flag)
I do a SELECT query as such :
SELECT badge_id, merchant_id, badge_title, badge_class
FROM badges WHERE merchant_id = 1 AND flag = 1
what index would be better? Does it matter if they are in a seperate index?
To answer questions such as "Which column would be better to index?", and "Is the query planner using a certain index to execute the query?", you can use the EXPLAIN statement. See the excellent article Analyzing Queries for Speed with EXPLAIN for a comprehensive overview of the use of EXPLAIN in optimizing queries and schema.
In general, where a query can be optimized by indexing one of several columns, a helpful rule of thumb is to index the column that is "most unique" or "most selective" over all records; that is, index the column that has the most number of distinct values over all rows. I am guessing that in your case, the merchant_id column contains the most number of unique values, so it should probably be indexed. You can verify that an index choice is optimal using EXPLAIN on the query for all variations.
Note that the rule of thumb "index the most selective column" does not necessarily apply to the choice of the first column of a composite (also called compound or multi-column) index. It depends on your queries. If, for example, employee_id is the most selective column, but you need to execute queries like SELECT * FROM badges WHERE flag = 17, then having as the only index on table badges the composite index (employee_id, flag) would mean that the query results in a full table scan.
Out of 3 indices you don't really need a separate merchant_id index, since merchant_id look-ups can use your "test" index.
More details:
http://dev.mysql.com/doc/refman/5.0/en/multiple-column-indexes.html
I have a table with 3 columns. This table contains many raws (millions). When I select rows from the table I frequently use the following where clauses:
where column2=value1 and column3=value2
where column1=value
To speed up the select query I want to declare column1 and column2 as indexes. My questions is if declaring the second column as an index will not reduce the positive effect of declaring the first column as index.
I also would like to ask if declaring the second column as index will speed up the queries of this type: where column2=value1 and column3=value2.
ADDED
The column1, column2, and column3 are entity, attribute, value. It's very general. As entities I use person, movies, cities, countries and so on. Attributes are things like: "located in", "date of birth", "produced by".
You should create indexes that support your queries. In this case you want to create an index on column2,column3 together (not two separate indexes, but one index for the combination of columns) to support the first query, and another on column1 to support the second query. More generally, if a query uses a set of columns, adding an index for all those columns will speed it up (although there are many exceptions, of course).
An index on column2 would speed up the query column2=value1 and column1=value2, and so would an index on column2,column3 (the important thing is that column2 is the first column in the index).
When working with indexes the EXPLAIN keyword is very useful. Prefix your queries with EXPLAIN (e.g. EXPLAIN SELECT * FROM table) to get a description of how the database is going to perform your query. It will tell you if it's going to use an index, and in that case which.
Seems like neither of your plans are going to work. Based on both of the where clauses I would suggest having the primary key on column1 and a second index column2,column3. This would speed up both of your queries.