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/
Related
I have a table with index on a int column.
Create table sample(
col1 varchar,
col2 int)
Create index idx1 on sample(col2);
When I explain the following query
Select * from sample where col2>2;
It does a full table scan.
Why doesn't the indexing work here?
How can i optimize such queries when table has around 20 million records?
Just because you create an index, does not mean MySQL will always use it. According to the docs, here are several reasons why it may choose to use a full table scan over the index:
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.
There are no usable restrictions in the ON or WHERE clause for indexed columns.
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 8.2.1.1, “WHERE Clause Optimization”.
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.
You can use FORCE INDEX to ensure your query uses the index instead of allowing the optimizer to determine the appropriate path, although usually MySQL will take the most efficient approach.
SELECT * FROM t1, t2 FORCE INDEX (index_for_column) WHERE t1.col_name=t2.col_name;
Reference: https://dev.mysql.com/doc/refman/8.0/en/table-scan-avoidance.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 have a table with the following columns:
id-> PK
customer_id-> index
store_id-> index
order_date-> index
last_modified-> index
other_columns...
other_columns...
I have three single column index. I also have a customer_id_store_id index which is a foreign key constraint referencing other tables.
id, customer_id, store_id are char(36) which is UUID. order_date is datetime and last_modifed is UNIX timestamp.
I want to gain some performance by removing all index and adding one with (customer_id, store_id, order_date). Most queries will have these fields in the where clause. But sometimes the store_id will not be needed.
What is the best approach? to add "store_id IS NOT NULL" in the where clause or creating the index this way (customer_id, order_date, store_id).
I also frequently need to query the table by last_modified field (where clause includes customer_id=, store_id=, last_modified>).
As I only have a single column index on it and there are hundreds of customers who is insert/updating the tables, more often the index scans rows more than necessary. Is it better to create another index (customer_id, store_id, last_modified) or leave it as it is? Or add this column to the previous index making it four columns composite index. But then again the order_date is irrelevant here and omitting it might result the index not being used as intended.
The query works fast on customers that don't have many rows possibly using the customer_id index there. But for customers with large amount of data, this isn't optimal. More often I need only few days of data.
Can anyone please advise what's the best index in this scenario.
It is true that lots of single column indexes on a MySQL table are generally considered harmful.
A query with
WHERE customer_id=constant AND store_id=constant AND last_modified>=constant
will be accelerated by an index on (customer_id, store_id, last_modified). Why? The MySQL query planner can random-access the index to the first item it needs to retrieve, then scan the index sequentially. That same index works for
WHERE customer_id=constant AND store_id=constant
AND last_modified>=constant
AND last_modified< constant + INTERVAL 1 DAY
BUT, that index will not be useful for a query with just
WHERE store_id=constant AND last_modified>constant
or
WHERE customer_id=constant AND store_id IS NOT NULL AND last_modified>=constant
For the first of those query patterns you need (store_id, last_modified) to achieve the ability to sequentially scan the index.
The second of those query patterns requires two different range searches. One is something IS NOT NULL. That's a range search because it has to romp through all the non-null values in the column. The second range search is last_modified>=constant. That's a range search, because it starts with the first value of last_modified that meets the given criterion, and scans to the end of the index.
MySQL indexes are B-trees. That means, essentially, that they're sorted into a particular single order. So, an index is best for accelerating queries that require just one range search. So, the second query pattern is inherently hard to satisfy with an index.
A table can have multiple compound indexes designed to satisfy multiple different query patterns. That's usually the strategy to large tables work well in practical applications. Each index imposes a little bit of performance penalty on updates and inserts. Indexes also take storage space. But storage is very cheap these days.
If you want to use a compound index to search on multiple criteria, these things must be true:
all but one of the criteria must be equality criteria like store_id = constant.
one criterion can be a range-scan criterion like last_modified >= constant or something IS NOT NULL.
the columns in the index must be ordered so that the columns involved in equality criteria all appear, then the the column involved in the range-scan criterion.
you may mention other columns after the range scan criterion. But they make up part of a covering index strategy (beyond the scope of this post).
http://use-the-index-luke.com/ is a good basic intro to the black art of indexing.
I have a table with two partitions. Partitions are pactive = 1 and pinactive = 0. I understand that two partitions does not make so much of a gain, but I have used it to truncate and load in one partition and plain inserts in another partition.
The problem comes when I create indexes.
Query goes this way
select partitionflag,companyid,activityname
from customformattributes
where companyid=47
and activityname = 'Activity 1'
and partitionflag=0
Created index -
create index idx_try on customformattributes(partitionflag,companyid,activityname,completiondate,attributename,isclosed)
there are around 200000 records that will be retreived from the above query. But the query along with the mentioned index takes 30+ seconds. What is the reason for such a long time? Also, if remove the partitionflag from the mentioned index, the index is not even used.
And is the understanding that,
Even with the partitions available, the optimizer needs to have the required partition mentioned in the index definition, so that it only hits the required partition ---- Correct?
Any ideas on understanding this would be very helpful
You can optimize your index by reordering the columns in it. Usually the columns in the index are ordered by its cardinality (starting from the highest and go down to the lowest). Cardinality is the uniqueness of data in the given column. So in your case I suppose there are many variations of companyid in customformattributes table while partitionflag will have cardinality of 2 (if all the options for this column are 1 and 0).
Your query will first filter all the rows with partitionflag=0, then it will filter by company id and so on.
When you remove partitionflag from the index the query did not used the index because may be the optimizer decides that it will be faster to make full table scan instead of using the index (in most of the cases the optimizer is right)
For the given query:
select partitionflag,companyid,activityname
from customformattributes
where companyid=47
and activityname = 'Activity 1'
and partitionflag=0
the following index may be would be better (but of course :
create index idx_try on customformattributes(companyid,activityname, completiondate,attributename, partitionflag, isclosed)
For the query to use index the following rule must be met - the left most column in the index should be present in the where clause ... and depending on the mysql version you are using additional query requirements may be needed. For example if you are using old version of mysql - you may need to order the columns in the where clause in the same order they are listed in the index. In the last versions of mysql the query optimizer is responsible for ordering the columns in the where clause in the correct order.
Your SELECT query took 30+ seconds because it returns 200k rows and because the index might not be the optimal for the given query.
For the second question about the partitioning: the common rule is that the column you are partitioning by must be part of all the UNIQUE keys in a table (Primary key is also unique key by definition so the column should be added to the PK also). If table structure and logic allows you to add the partitioning column to all the UNIQUE indexes in the table then you add it and partition the table.
When the partitioning is made correctly you can take the advantage of partitioning pruning - this is when SELECT query searches the data only in the partitions where given data is stored (otherwise it looks in all partitions)
You can read more about partitioning here:
https://dev.mysql.com/doc/refman/5.6/en/partitioning-overview.html
The query is slow simply because disks are slow.
Cardinality is not important when designing an index.
The optimal index for that query is
INDEX(companyid, activityname, partitionflag) -- in any order
It is "covering" since it includes all the columns mentioned anywhere in the SELECT. This is indicated by "Using index" in the EXPLAIN.
Leaving off the other 3 columns makes the query faster because it will have to read less off the disk.
If you make any changes to the query (add columns, change from '=' to '>', add ORDER BY, etc), then the index may no longer be optimal.
"Also, if remove the partitionflag from the mentioned index, the index is not even used." -- That is because it was no longer "covering".
Keep in mind that there are two ways an index may be used -- "covering" versus being a way to look up the data. When you don't have a "covering" index, the optimizer chooses between using the index and bouncing between the index and the data versus simply ignoring the index and scanning the table.
Making my concept more clear about indexes in Mysql. What i know is indexes are used to make your query faster. except that i have couple of questions to know about.
Let's say i'm having a query:
SELECT books.name, books.name2, books.id, books.image, books.faith, books.topic, books.downloaded, books.viewed, books.language, books.size, books.author as author_id, authors.name as author_name, authors.aid from books LEFT JOIN authors ON books.author = authors.aid WHERE books.id = '".$id."' AND status = 1
Is any index applicable for this select query while it has JOIN?
After making an index on a column query for that will be optimized
or changed ?
How to make index for this query and against which column?
What's the other benefits or disadvantages of using indexes?
On which case indexes should be avoided and where should use indexes
for more?
Do indexes applicable on random queries ?
Are indexes more efficient on IDS ?
Please apprise, thank you in advanced !
You can check details on below links.
https://dev.mysql.com/doc/refman/5.0/en/mysql-indexes.html
http://mydbsolutions.in/query-optimization-2/
Your queries are here-
Is any index applicable for this select query while it has JOIN?
It will depend on various factors like if your table have very less data or approx. 70% data is same in index column then mysql will prefer to scan table instead of index. In simple all your join columns should be indexed (will be indexed if you use foreign key concept other wise you should indexed them). Also on which column your query is filtering most data that field should be indexed. In your case you are filtering data on books.id which should be primary key so already indexed.
After making an index on a column query for that will be optimized or changed?
It will be automatically start to use index but in some cases may be you need to change your query. Suppose you are using a filter condition as "date(order_date)='2015-10-15'", even after creating index on order_date it will not be used so you have to change your query as "order_date>='2015-10-15 00:00:00' and order_date<='2015-10-15 23:59:59'" if you order_date column data type is datetime or timestamp.
How to make index for this query and against which column?
Here I am not seeing any need of making index as your condition is on books table primary key and it will be already indexed.
What's the other benefits or disadvantages of using indexes?
If you create index blindly then at the time of record insertion/updation etc each time index will be updated and will slow the process. Even heavy index will perform slow. Also will consume more disk space.
On which case indexes should be avoided and where should use indexes for more?
If more than 70% data is same for any column then no need to create index on them like status or is_deleted type columns as mostly data will be active.
Do indexes applicable on random queries ?
Yes index work on random queries, for repeatable queries you can use query cache which will be more efficient.
Are indexes more efficient on IDS ?
Yes.