For a few days, I've been struggling with improving the performance of my database and there are some issues that I'm still kind a confused about regarding indexing in a SQL Server database.
I'll try to be as informative as I can.
My database currently contains about 100k rows and will keep growing, therfore I'm trying to find a way to make it work faster.
I'm also writing to this table, so if you suggestion will drastically reduce the writing time please let me know.
Overall goal is to select all rows with a specific names that are in a date range.
That will usually be to select over 3,000 rows out of a lot lol ...
Table schema:
CREATE TABLE [dbo].[reports]
(
[id] [int] IDENTITY(1,1) NOT NULL,
[IsDuplicate] [bit] NOT NULL,
[IsNotValid] [bit] NOT NULL,
[Time] [datetime] NOT NULL,
[ShortDate] [date] NOT NULL,
[Source] [nvarchar](350) NULL,
[Email] [nvarchar](350) NULL,
CONSTRAINT [PK_dbo.reports]
PRIMARY KEY CLUSTERED ([id] ASC)
) ON [PRIMARY]
This is the SQL query I'm using:
SELECT *
FROM [db].[dbo].[reports]
WHERE Source = 'name1'
AND ShortDate BETWEEN '2017-10-13' AND '2017-10-15'
As I understood, my best approach to improve efficency without hurting the writing time as much would be to create a nonclustered index on the Source and ShortDate.
Which I did like such, index schema:
CREATE NONCLUSTERED INDEX [Source&Time]
ON [dbo].[reports]([Source] ASC, [ShortDate] ASC)
Now we are getting to the tricky part which got me completely lost, the index above sometimes works, sometime half works and sometime doesn't work at all....
(not sure if it matters but currently 90% of the database rows has the same Source, although this won't stay like that for long)
With the query below, the index isn't used at all, I'm using SQL Server 2014 and in the Execution Plan it says it only uses the clustered index scan:
SELECT *
FROM [db].[dbo].[reports]
WHERE Source = 'name1'
AND ShortDate BETWEEN '2017-10-10' AND '2017-10-15'
With this query, the index isn't used at all, although I'm getting a suggestion from SQL Server to create an index with the date first and source second... I read that the index should be made by the order the query is? Also it says to include all the columns Im selecting, is that a must?... again I read that I should include in the index only the columns I'm searching.
SELECT *
FROM [db].[dbo].[reports]
WHERE Source = 'name1'
AND ShortDate = '2017-10-13'
SQL Server index suggestion -
/* The Query Processor estimates that implementing the following
index could improve the query cost by 86.2728%. */
/*
USE [db]
GO
CREATE NONCLUSTERED INDEX [<Name of Missing Index, sysname,>]
ON [dbo].[reports] ([ShortDate], [Source])
INCLUDE ([id], [IsDuplicate], [IsNotValid], [Time], [Email])
GO
*/
Now I tried using the index SQL Server suggested me to make and it works, seems like it uses 100% of the nonclustered index using both the queries above.
I tried to use this index but deleting the included columns and it doesn't work... seems like I must include in the index all the columns I'm selecting?
BTW it also work when using the index I made if I include all the columns.
To summarize: seems like the order of the index didn't matter, as it worked both when creating Source + ShortDate and ShortDate + Source
But for some reason its a must to include all the columns... (which will drastically affect the writing to this table?)
Thanks a lot for reading, My goal is to understand why this stuff happens and what I should do otherwise (not just the solution as I'll need to apply it on other projects as well ).
Cheers :)
Indexing in SQL Server is part know-how from long experience (and many hours of frustration), and part black magic. Don't beat yourself up over that too much - that's what a place like SO is ideal for - lots of brains, lots of experience from many hours of optimizing, that you can tap into.
I read that the index should be made by the order the query is?
If you read this - it is absolutely NOT TRUE - the order of the columns is relevant - but in a different way: a compound index (made up from multiple columns) will only ever be considered if you specify the n left-most columns in the index definition in your query.
Classic example: a phone book with an index on (city, lastname, firstname). Such an index might be used:
in a query that specifies all three columns in its WHERE clause
in a query that uses city and lastname (find all "Miller" in "Detroit")
or in a query that only filters by city
but it can NEVER EVER be used if you want to search only for firstname ..... that's the trick about compound indexes you need to be aware of. But if you always use all columns from an index, their ordering is typically not really relevant - the query optimizer will handle this for you.
As for the included columns - those are stored only in the leaf level of the nonclustered index - they are NOT part of the search structure of the index, and you cannot specify filter values for those included columns in your WHERE clause.
The main benefit of these included columns is this: if you search in a nonclustered index, and in the end, you actually find the value you're looking for - what do you have available at that point? The nonclustered index will store the columns in the non-clustered index definition (ShortDate and Source), and it will store the clustering key (if you have one - and you should!) - but nothing else.
So in this case, once a match is found, and your query wants everything from that table, SQL Server has to do what is called a Key lookup (often also referred to as a bookmark lookup) in which it takes the clustered key and then does a Seek operation against the clustered index, to get to the actual data page that contains all the values you're looking for.
If you have included columns in your index, then the leaf level page of your non-clustered index contains
the columns as defined in the nonclustered index
the clustering key column(s)
all those additional columns as defined in your INCLUDE statement
If those columns "cover" your query, e.g. provide all the values that your query needs, then SQL Server is done once it finds the value you searched for in the nonclustered index - it can take all the values it needs from that leaf-level page of the nonclustered index, and it does NOT need to do another (expensive) key lookup into the clustering index to get the actual values.
Because of this, trying to always explicitly specify only those columns you really need in your SELECT can be beneficial - in this case, you might be able to create an efficient covering index that provides all the values for your SELECT - always using SELECT * makes that really hard or next to impossible.....
In general, you want the index to be from most selective (i.e. filtering out the most possible records) to least selective; if a column has low cardinality, the query optimizer may ignore it.
That makes intuitive sense - if you have a phone book, and you're looking for people called "smith", with the initial "A", you want to start with searching for "smith" first, and then the "A"s, rather than all people whose initial is "A" and then filter out those called "Smith". After all, the odds are that one in 26 people have the initial "A".
So, in your example, I guess you have a wide range of values in short date - so that's the first column the query optimizer is trying to filter out. You say you have few different values in "source", so the query optimizer may decide to ignore it; in that case, the second column in that index is no use either.
The order of where clauses in the index is irrelevant - you can swap them round and achieve the exact same results, so the query optimizer ignores them.
EDIT:
So, yes, make the index. Imagine you have a pile of cards to sort - in your first run, you want to remove as many cards as possible. Assuming it's all evenly spread - if you have 1000 separate short_dates over a million rows, that means you end up with 1000 items if your first run starts on short_date; if you sort by source, you have 100000 rows.
The included columns of an index is for the columns you are selecting.
Due to the fact that you do select * (which isn't good practice), the index won't be used, because it would have to lookup the whole table to get the values for the columns.
For your scenario, I would drop the default clustered index (if there is one) and create a new clustered index with the following statement:
USE [db]
GO
CREATE CLUSTERED INDEX CIX_reports
ON [dbo].[reports] ([ShortDate],[Source])
GO
I have a question:
Definition of non-clustered index says that included columns in index are not counted by database engine in sense of index size or maximum number of columns.
So what is really the way they work?
How they help to SQL Server when they are not acting in index size?
The important thing to note is that included columns are not counted by the database engine when determining the size or number of columns in the index key (the value used to actually look up data in the index structure). They still add to the size of the index itself.
Index keys are only allowed to be 900 bytes in size across all columns that make up the key (there can be only 16 columns that make up the index key).
Adding included columns doesn't count towards the 900 byte/16 column limits, but can make the index more useful by covering more queries.
Good explanations from the other people here.
For me, included index columns are rather easy to remember and use with this simple rule:
Filters, ie. WHERE x = y etc.., are your keys, the decision whether to use the index or not is based on those. SELECT a, b, x are the values you're actually returning, those are the things you want to include in your index so SQL Server doesn't have to go searching through the clustered index / heap to find them.
Example:
CREATE NONCLUSTERED INDEX TABLEX_A_IDX ON TABLEX (A) INCLUDE (B, C)
SELECT A, B, C -- KEY + INCLUDED columns
FROM TABLEX WHERE A = 'ASD' -- KEY columns
Granted, this wasn't exactly your question, but it might help just the same.
when I create an index on a db2, for example with the following code:
CREATE INDEX T_IDX ON T(
A,
B)
is it a composite index?
if not: how can I then create a composite index?
if yes: in order to have two different index should I create them separately as:
CREATE INDEX T1_IDX ON T(A)
CREATE INDEX T2_IDX ON T(A)
EDIT: this discussion is not going in the direction I expect (but in a better one :)) I actually asked how, and not why to create separate indexes, I planed to do that in a different question, but since you anticipated me:
suppose I have a table T(A,B,C) and a search function search() that select from the table using any of the following method
WHERE A = x
WHERE B = x
WHERE C = x
WHERE A = x AND B=y (and so on AC, CB, ABC)
if I create a compose index ABC, is it going to working for example when I select on just C?
the table is quite big, and the insert\update not so frequent
Yep multiple fields on create index = composite by definition: Specify two or more column names to create a composite index.
Understanding when to use composite indexes appears to be your last question...
If all columns selected by a query are in a composite index, then the dbengine can return these values from the index without accessing the table. so you have faster seek time.
However if one or the other are used in queries, then creating individual indexes will serve you best. It depends on the types of queries executed and what values they contain/filter/join.
If you sometimes have one, the other, or both, then creating all 3 indexes is a possibility as well. But keep in mind each additional index increases the amount of time it takes to insert, update or delete, so on highly maintained tables, more indexes are generally bad since the overhead to maintain the indexes effects performance.
The index on A, B is a composite index, and can be used to seek on just A or a seek on A with B or for a general scan, of course.
There is usually not much of a point in having an index on A, B and an index on just A, since a partial search on A, B can be used if you only have A. That wider index will be a little less efficient, however, so if the A lookup is extremely frequent and the write requirements mean that it is acceptable to update the extra index, it could be justifiable.
Having an index on B may be necessary, since the A, B index is not very suitable for searches based on B only.
First Answer: YES
CREATE INDEX JOB_BY_DPT
ON EMPLOYEE (WORKDEPT, JOB)
Second Answer:
It depends on your query; if most of the time your query referrence a single column in where clause like select * from T where A = 'something' then a single index would be what you want but if both column A and B get referrenced then you should go for creating a composite one.
For further referrence please check
http://publib.boulder.ibm.com/infocenter/db2luw/v8/index.jsp?topic=/com.ibm.db2.udb.doc/admin/r0000919.htm
In a former company I worked at, the rule of thumb was that a table should have no more than one index (allowing the odd exception, and certain parent-tables holding references to nearly all other tables and thus are updated very frequently).
The idea being that often, indexes cost the same or more to uphold than they gain. Note that this question is different to indexed-view-vs-indexes-on-table as the motivation is not only reporting.
Is this true? Is this index-purism worth it?
In your career do you generally avoid using indexes?
What are the general large-scale recommendations regarding indexes?
Currently and at the last company we use SQL Server, so any product specific guidelines are welcome too.
You need to create exactly as many indexes as you need to create. No more, no less. It is as simple as that.
Everybody "knows" that an index will slow down DML statements on a table. But for some reason very few people actually bother to test just how "slow" it becomes in their context. Sometimes I get the impression that people think that adding another index will add several seconds to each inserted row, making it a game changing business tradeoff that some fictive hotshot user should decide in a board room.
I'd like to share an example that I just created on my 2 year old pc, using a standard MySQL installation. I know you tagged the question SQL Server, but the example should be easily converted. I insert 1,000,000 rows into three tables. One table without indexes, one table with one index and one table with nine indexes.
drop table numbers;
drop table one_million_rows;
drop table one_million_one_index;
drop table one_million_nine_index;
/*
|| Create a dummy table to assist in generating rows
*/
create table numbers(n int);
insert into numbers(n) values(0),(1),(2),(3),(4),(5),(6),(7),(8),(9);
/*
|| Create a table consisting of 1,000,000 consecutive integers
*/
create table one_million_rows as
select d1.n + (d2.n * 10)
+ (d3.n * 100)
+ (d4.n * 1000)
+ (d5.n * 10000)
+ (d6.n * 100000) as n
from numbers d1
,numbers d2
,numbers d3
,numbers d4
,numbers d5
,numbers d6;
/*
|| Create an empty table with 9 integer columns.
|| One column will be indexed
*/
create table one_million_one_index(
c1 int, c2 int, c3 int
,c4 int, c5 int, c6 int
,c7 int, c8 int, c9 int
,index(c1)
);
/*
|| Create an empty table with 9 integer columns.
|| All nine columns will be indexed
*/
create table one_million_nine_index(
c1 int, c2 int, c3 int
,c4 int, c5 int, c6 int
,c7 int, c8 int, c9 int
,index(c1), index(c2), index(c3)
,index(c4), index(c5), index(c6)
,index(c7), index(c8), index(c9)
);
/*
|| Insert 1,000,000 rows in the table with one index
*/
insert into one_million_one_index(c1,c2,c3,c4,c5,c6,c7,c8,c9)
select n, n, n, n, n, n, n, n, n
from one_million_rows;
/*
|| Insert 1,000,000 rows in the table with nine indexes
*/
insert into one_million_nine_index(c1,c2,c3,c4,c5,c6,c7,c8,c9)
select n, n, n, n, n, n, n, n, n
from one_million_rows;
My timings are:
1m rows into table without indexes: 0,45 seconds
1m rows into table with 1 index: 1,5 seconds
1m rows into table with 9 indexes: 6,98 seconds
I'm better with SQL than statistics and math, but I'd like to think that:
Adding 8 indexes to my table, added (6,98-1,5) 5,48 seconds in total. Each index would then have contributed 0,685 seconds (5,48 / 8) for all 1,000,000 rows. That would mean that the added overhead per row per index would have been 0,000000685 seconds. SOMEBODY CALL THE BOARD OF DIRECTORS!
In conclusion, I'd like to say that the above test case doesn't prove a shit. It just shows that tonight, I was able to insert 1,000,000 consecutive integers into in a table in a single user environment. Your results will be different.
That is utterly ridiculous. First, you need multiple indexes in order to perfom correctly. For instance, if you have a primary key, you automatically have an index. that means you can't index anything else with the rule you described. So if you don't index foreign keys, joins will be slow and if you don't index fields used in the where clause, queries will still be slow. Yes you can have too many indexes as they do take extra time to insert and update and delete records, but no more than one is not dangerous, it is a requirement to have a system that performs well. And I have found that users tolerate a longer time to insert better than they tolerate a longer time to query.
Now the exception might be for a system that takes thousands of readings per second from some automated equipment. This is a database that generally doesn't have indexes to speed inserts. But usually these types of databases are also not used for reading, the data is transferred instead daily to a reporting database which is indexed.
Yes, definitely - too many indexes on a table can be worse than no indexes at all. However, I don't think there's any good in having the "at most one index per table" rule.
For SQL Server, my rule is:
index any foreign key fields - this helps JOINs and is beneficial to other queries, too
index any other fields when it makes sense, e.g. when lots of intensive queries can benefit from it
Finding the right mix of indices - weighing the pros of speeding up queries vs. the cons of additional overhead on INSERT, UPDATE, DELETE - is not an exact science - it's more about know-how, experience, measuring, measuring, and measuring again.
Any fixed rule is bound to be more contraproductive than anything else.....
The best content on indexing comes from Kimberly Tripp - the Queen of Indexing - see her blog posts here.
Unless you like very slow reads, you should have indexes. Don't go overboard, but don't be afraid of being liberal about them either. EVERY FK should be indexed. You're going to do a look up each of these columns on inserts to other tables to make sure the references are set. The index helps. As well as the fact that indexed columns are used often in joins and selects.
We have some tables that are inserted into rarely, with millions of records. Some of these tables are also quite wide. It's not uncommon for these tables to have 15+ indexes. Other tables with heavy inserting and low reads we might only have a handful of indexes- but one index per table is crazy.
Updating an index is once per insert (per index). Speed gain is for every select. So if you update infrequently and read often, then the extra work may be well worth it.
If you do different selects (meaning the columns you filter on are different), then maintaining an index for each type of query is very useful. Provided you have a limited set of columns that you query often.
But the usual advice holds: if you want to know which is fastest: profile!
You should of course be careful not to create too many indexes per table, but only ever using a single index per table is not a useful level.
How many indexes to use depends on how the table is used. A table that is updated often would generally have less indexes than one that is read much more often than it's updated.
We have some tables that are updated regularly by a job every two minutes, but they are read often by queries that vary a lot, so they have several indexes. One table for example have 24 indexes.
So much depends on your schema and the queries that you normally run. For example: if you normally need to select above 60% of the rows of your table, indexes won't help you and it will be cheaper to table scan than to index scan and then lookup rows. Focused queries that select a small number of rows in different parts of the table or which are used for joins in queries will probably benefit from indexes. The right index in the right place can make or break a feature.
Indexes take space so making too many indexes on a table can be counter productive for the same reasons listed above. Scanning 5 indexes and then performing row lookups may be much more expensive than simply table scanning.
Good design is the synthesis about about knowing when to normalise and when not to.
If you frequently join on a particular column, check the IO plan with the index and without. As a general rule I avoid tables with more than 20 columns. This is often a sign that the data should be normalised. More than about 5 indexes on a table and you may be using more space for the indexes than the main table, be sure that is worth it. These rules are only the lightest of guidance and so much depends on how the data will be used in queries and what your data update profile looks like.
Experiment with your query plans to see how your solution improves or degrades with an index.
Every table must have a PK, which is indexed of course (generally a clustered one), then every FK should be indexed as well.
Finally you may want to index fields on which you often sort on, if their data is well differenciated: for a field with only 5 possible values in a table with 1 million records, an index will not be of a great benefit.
I tend to be minimalistic with indexes, until the db starts beeing well filled, and ...slower. It is easy to identify the bottlenecks and add just the right the indexes at that point.
Optimizing the retrieval with indexes must be carefully designed to reflect actual query patterns. Surely, for a table with Primary Key, you will have at least one clustered index (that's how data is actually stored), then any additional indexes are taking advantage of the layout of the data (clustered index).
After analyzing queries that execute against the table, you want to design an index(s) that cover them. That may mean building one or more indexes but that heavily depends on the queries themselves. That decision cannot be made just by looking at column statistics only.
For tables where it's mostly inserts, i.e. ETL tables or something, then you should not create Primary Keys, or actually drop indexes and re-create them if data changes too quickly or drop/recreated entirely.
I personally would be scared to step into an environment that has a hard-coded rule of indexes per table ratio.