SQL LIKE with parameter : huge performance hit - sql-server

I have this query :
select top 100 id, email, amount from view_orders
where email LIKE '%test%' order by created_at desc
It takes less than a second to run.
Now I want to parameterize it :
declare #m nvarchar(200)
set #m = '%test%'
SELECT TOP 100 id, email, amount FROM view_orders
WHERE email LIKE #m ORDER BY created_at DESC
After 5 minutes, it's still running. With any other kind of test on parameters (if I replace the "like" with "="), it falls down to the first query level of performance.
I am using SQL Server 2008 R2.
I have tried with OPTION(RECOMPILE) , it drops to 6 seconds, but it's still much slower (the non-parameterized query is instantaneous). As it's a query that I expect will be run often, it's an issue.
The table's column is indexed, but the view is not, I don't know if it can make a difference.
The view joins 5 tables : one with 3,154,333 rows (users), one with 1,536,111 rows (orders), and 3 with a few dozen rows at most (order type, etc). The search is done on the "user" table (with 3M rows).
Hard-coded values :
Parameters :
Update
I have run the queries using SET STATISTICS IO ON. Here are the result (sorry I don't know how to read that) :
Hard-coded values:
Table 'currency'. Scan count 1, logical reads 201.
Table 'order_status'. Scan count 0, logical reads 200.
Table 'payment'. Scan count 1, logical reads 100.
Table 'gift'. Scan count 202, logical reads 404.
Table 'order'. Scan count 95, logical reads 683.
Table 'user'. Scan count 1, logical reads 7956.
Parameters :
Table 'currency'. scan count 1, logical reads 201.
Table 'order_status'. scan count 1, logical reads 201.
Table 'payment'. scan count 1, logical reads 100.
Table 'gift'. scan count 202, logical reads 404.
Table 'user'. scan count 0, logical reads 4353067.
Table 'order'. scan count 1, logical reads 4357031.
Update 2
I have since seen a "force index usage" hint :
SELECT TOP 100 id, email, amount
FROM view_orders with (nolock, index=ix_email)
WHERE email LIKE #m
ORDER BY created_at DESC
Not sure it would work though, I don't work at this place anymore.

It could be a parameter sniffing problem.
Better indexes or a full text search are the way to go but you might be able to get a workable compromise.
Try:
SELECT TOP 100 A, B, C FROM myview WHERE A LIKE '%' + #a + '%'
OPTION (OPTIMIZE FOR (#a = 'testvalue'));
(like Sean Coetzee suggests, I wouldn't pass in the wildcard in the parameter)

You will definetly win when you add an index to the A column.
Some time the index suggestion can be borrowed by SQL Server management studio. Paste you query and press Display Estimated Execution Plan button

CREATE INDEX index_name ON myview (A);
CREATE INDEX index_name ON myview (B);
CREATE INDEX index_name ON myview (C);
declare #a nvarchar(200)
set #a = '%testvalue%'
SELECT TOP 100 A, B, C FROM myview WHERE A LIKE #a

What happens if you try:
set #a = 'test'
select top 100 A, B, C
from myview
where A like '%' + #a + '%'
I've tried a test on some dummy data and it looks like it may be faster.

The estimated execution plan for the parameterized version is clearly not right. I don't believe I've seen a query with 100% estimated cost twice! As the cost is supposed to total 100%. It's also interesting that it believes it needs to start with orders when you're clearly filtering by something on the user table.
I'd rebuild your statistics on all of the tables that are referenced in the view.
update statistics <tablename> with resample
Do one of these for each table involved.
You can attempt running the sql directly (copy paste view body into sql) both parameterized and not to see if it's the view sql is having issues with.
At the end of the day even when you get this fixed it's really only a stop gap. You have 3million users and every time you run the query sql has to go through all 3million records (the 75% scan in your top query) to find all the possible records. The more users you get the slower the query gets. Non-fulltext indexes can't be used for a like query with wildcards at the front.
In this case you can think about a sql index like a book index. Can you use a book index with "part" of a word to find anything quickly? Nope, you've got to scan the whole index to figure out all the possibilities.
You should really consider a full text index on your view.

Related

Slow two-table query in SQL Server

The application I work on generates an SQL query somewhat like this:
Select
VISIT_VIEW.VISIT_ID, VISIT_VIEW.PATIENT_ID, VISIT_VIEW.MRN_ID,
VISIT_VIEW.BILL_NO, INSURANCE.INS_PAYOR'
FROM
'VISIT_VIEW
LEFT JOIN
INSURANCE ON VISIT_VIEW.visit_id = INSURANCE._fk_visit '
WHERE
'VISIT_VIEW.VISIT_ID IN (1002, 1003, 1005, 1006, 1007, 1008, 1010, 1011, <...>, 1193, 1194, 1195, 1196, 1197, 1198, 1199)'
The <...> represents a long list of ids. The size of the list depends on the results of a previous query, and in turn on the parameters selected to generate that query.
The list of IDs can be anywhere from 100 items long to above 2000.
The INSURANCE table is large, over 9 million rows. The visit table is also large, but not quite as large.
As the number of IDs goes up there is a fairly sharp increase from a duration of less than a second to over 15 minutes. The increase starts somewhere around 175 ids.
If the parameters used to generate the query are changed so that the INS_PAYOR column is not selected, and thus there is no left join, the query runs in less than a second, even with over 2000 items in the list of IDs.
The execution plan shows that 97% of the query time is devoted to a clustered seek on the INSURANCE table.
How can I rework this query to get the same results with a less horrific delay?
Do remember that the SQL is being generated by code, not by hand. It is generated from a list of fields (with knowledge of which field belongs to which table) and a list of IDs in the primary table to check. I do have access to the code that does the query generation, and can change it provided that the ultimate results of the query are exactly the same.
Thank you
The <...> represents a long list of ids. The size of the list depends on the results of a previous query
Don't do that.
Do this:
SELECT <...>
FROM VISIT_VIEW
INNER JOIN (
<previous query goes here>
) t on VISIT_VIEW.VISIT_ID = t.<ID?>
LEFT JOIN INSURANCE ON VISIT_VIEW.visit_id=INSURANCE._fk_visit
See if you see any improvements using the following...
IF OBJECT_ID('tempdb..#VisitList', 'U') IS NOT NULL
DROP TABLE #VisitList;
CREATE TABLE #VisitList (
VISIT_ID INT NOT NULL PRIMARY KEY
);
INSERT #VisitList (VISIT_ID) VALUES (1002),(1003),(1005),(1006),(1007),(1008),(1010),(1011),(<...>),(1193),(1194),(1195),(1196),(1197),(1198),(1199);
SELECT
vv.VISIT_ID,
vv.PATIENT_ID,
vv.MRN_ID,
vv.BILL_NO,
ix.INS_PAYOR
FROM
VISIT_VIEW vv
JOIN #VisitList vl
ON vv.VISIT_ID = vl.VISIT_ID
CROSS APPLY (
SELECT TOP 1
i.INS_PAYOR
FROM
INSURANCE i
WHERE
vv.visit_id=i._fk_visit
) ix;

Using SQLServer contains for partial words

We are running many products search on a huge catalog with partially matched barcodes.
We started with a simple like query
select * from products where barcode like '%2345%'
But that takes way too long since it requires a full table scan.
We thought a fulltext search will be able to help us here using contains.
select * from products where contains(barcode, '2345')
But, it seems like contains doesn't support finding words that partially contains a text but, only full a word match or a prefix. (But in this example we're looking for '123456').
My answer is: #DenisReznik was right :)
ok, let's take a look.
I have worked with barcodes and big catalogs for many years and I was curious about this question.
So I have made some tests on my own.
I have created a table to store test data:
CREATE TABLE [like_test](
[N] [int] NOT NULL PRIMARY KEY,
[barcode] [varchar](40) NULL
)
I know that there are many types of barcodes, some contains only numbers, other contains also letters, and other can be even much complex.
Let's assume our barcode is a random string.
I have filled it with 10 millions records of random alfanumeric data:
insert into like_test
select (select count(*) from like_test)+n, REPLACE(convert(varchar(40), NEWID()), '-', '') barcode
from FN_NUMBERS(10000000)
FN_NUMBERS() is just a function I use in my DBs (a sort of tally_table)
to get records quick.
I got 10 million records like that:
N barcode
1 1C333262C2D74E11B688281636FAF0FB
2 3680E11436FC4CBA826E684C0E96E365
3 7763D29BD09F48C58232C7D33551E6C9
Let's declare a var to search for:
declare #s varchar(20) = 'D34F15' -- random alfanumeric string
Let's take a base try with LIKE to compare results to:
select * from like_test where barcode like '%'+#s+'%'
On my workstation it takes 24.4 secs for a full clustered index scan. Very slow.
SSMS suggests to add an index on barcode column:
CREATE NONCLUSTERED INDEX [ix_barcode] ON [like_test] ([barcode]) INCLUDE ([N])
500Mb of index, I retry the select, this time 24.0 secs for the non clustered index seek.. less than 2% better, almost the same result. Very far from the 75% supposed by SSMS. It seems to me this index really doesn't worth it. Maybe my SSD Samsung 840 is making the difference..
For the moment I let the index active.
Let's try the CHARINDEX solution:
select * from like_test where charindex(#s, barcode) > 0
This time it took 23.5 second to complete, not really so much better than LIKE.
Now let's check the #DenisReznik 's suggestion that using the Binary Collation should speed up things.
select * from like_test
where barcode collate Latin1_General_BIN like '%'+#s+'%' collate Latin1_General_BIN
WOW, it seems to work! Only 4.5 secs this is impressive! 5 times better..
So, what about CHARINDEX and Collation toghether? Let's try it:
select * from like_test
where charindex(#s collate Latin1_General_BIN, barcode collate Latin1_General_BIN)>0
Unbelivable! 2.4 secs, 10 times better..
Ok, so far I have realized that CHARINDEX is better than LIKE, and that Binary Collation is better than normal string collation, so from now on I will go on only with CHARINDEX and Collation.
Now, can we do anything else to get even better results? Maybe we can try reduce our very long strings.. a scan is always a scan..
First try, a logical string cut using SUBSTRING to virtually works on barcodes of 8 chars:
select * from like_test
where charindex(
#s collate Latin1_General_BIN,
SUBSTRING(barcode, 12, 8) collate Latin1_General_BIN
)>0
Fantastic! 1.8 seconds.. I have tried both SUBSTRING(barcode, 1, 8) (head of the string) and SUBSTRING(barcode, 12, 8) (middle of the string) with same results.
Then I have tried to phisically reduce the size of the barcode column, almost no difference than using SUBSTRING()
Finally I have tried to drop the index on barcode column and repeated ALL above tests...
I was very surprised to get almost same results, with very little differences.
Index performs 3-5% better, but at cost of 500Mb of disk space and and maintenance cost if the catalog is updated.
Naturally, for a direct key lookup like where barcode = #s with the index it takes 20-50 millisecs, without index we can't get less than 1.1 secs using Collation syntax where barcode collate Latin1_General_BIN = #s collate Latin1_General_BIN
This was interesting.
I hope this helps
I often use charindex and just as often have this very debate.
As it turns out, depending on your structure you may actually have a substantial performance boost.
http://cc.davelozinski.com/sql/like-vs-substring-vs-leftright-vs-charindex
The good option here for your case - creating your FTS index. Here is how it could be implemented:
1) Create table Terms:
CREATE TABLE Terms
(
Id int IDENTITY NOT NULL,
Term varchar(21) NOT NULL,
CONSTRAINT PK_TERMS PRIMARY KEY (Term),
CONSTRAINT UK_TERMS_ID UNIQUE (Id)
)
Note: index declaration in the table definition is a feature of 2014. If you have a lower version, just bring it out of CREATE TABLE statement and create separately.
2) Cut barcodes to grams, and save each of them to a table Terms. For example: barcode = '123456', your table should have 6 rows for it: '123456', '23456', '3456', '456', '56', '6'.
3) Create table BarcodeIndex:
CREATE TABLE BarcodesIndex
(
TermId int NOT NULL,
BarcodeId int NOT NULL,
CONSTRAINT PK_BARCODESINDEX PRIMARY KEY (TermId, BarcodeId),
CONSTRAINT FK_BARCODESINDEX_TERMID FOREIGN KEY (TermId) REFERENCES Terms (Id),
CONSTRAINT FK_BARCODESINDEX_BARCODEID FOREIGN KEY (BarcodeId) REFERENCES Barcodes (Id)
)
4) Save a pair (TermId, BarcodeId) for the barcode into the table BarcodeIndex. TermId was generated on the second step or exists in the Terms table. BarcodeId - is an identifier of the barcode, stored in Barcodes (or whatever name you use for it) table. For each of the barcodes, there should be 6 rows in the BarcodeIndex table.
5) Select barcodes by their parts using the following query:
SELECT b.* FROM Terms t
INNER JOIN BarcodesIndex bi
ON t.Id = bi.TermId
INNER JOIN Barcodes b
ON bi.BarcodeId = b.Id
WHERE t.Term LIKE 'SomeBarcodePart%'
This solution force all similar parts of barcodes to be stored nearby, so SQL Server will use Index Range Scan strategy to fetch data from the Terms table. Terms in the Terms table should be unique to make this table as small as possible. This could be done in the application logic: check existence -> insert new if a term doesn't exist. Or by setting option IGNORE_DUP_KEY for clustered index of the Terms table. BarcodesIndex table is used to reference Terms and Barcodes.
Please note that foreign keys and constraints in this solution are the points of consideration. Personally, I prefer to have foreign keys, until they hurt me.
After further testing and reading and talking with #DenisReznik I think the best option could be to add virtual columns to barcode table to split barcode.
We only need columns for start positions from 2nd to 4th because for the 1st we will use original barcode column and the last I think it is not useful at all (what kind of partial match is 1 char on 6 when 60% of records will match?):
CREATE TABLE [like_test](
[N] [int] NOT NULL PRIMARY KEY,
[barcode] [varchar](6) NOT NULL,
[BC2] AS (substring([BARCODE],(2),(5))),
[BC3] AS (substring([BARCODE],(3),(4))),
[BC4] AS (substring([BARCODE],(4),(3))),
[BC5] AS (substring([BARCODE],(5),(2)))
)
and then to add indexes on this virtual columns:
CREATE NONCLUSTERED INDEX [IX_BC2] ON [like_test2] ([BC2]);
CREATE NONCLUSTERED INDEX [IX_BC3] ON [like_test2] ([BC3]);
CREATE NONCLUSTERED INDEX [IX_BC4] ON [like_test2] ([BC4]);
CREATE NONCLUSTERED INDEX [IX_BC5] ON [like_test2] ([BC5]);
CREATE NONCLUSTERED INDEX [IX_BC6] ON [like_test2] ([barcode]);
now we can simply find partial matches with this query
declare #s varchar(40)
declare #l int
set #s = '654'
set #l = LEN(#s)
select N from like_test
where 1=0
OR ((barcode = #s) and (#l=6)) -- to match full code (rem if not needed)
OR ((barcode like #s+'%') and (#l<6)) -- to match strings up to 5 chars from beginning
or ((BC2 like #s+'%') and (#l<6)) -- to match strings up to 5 chars from 2nd position
or ((BC3 like #s+'%') and (#l<5)) -- to match strings up to 4 chars from 3rd position
or ((BC4 like #s+'%') and (#l<4)) -- to match strings up to 3 chars from 4th position
or ((BC5 like #s+'%') and (#l<3)) -- to match strings up to 2 chars from 5th position
this is HELL fast!
for search strings of 6 chars 15-20 milliseconds (full code)
for search strings of 5 chars 25 milliseconds (20-80)
for search strings of 4 chars 50 milliseconds (40-130)
for search strings of 3 chars 65 milliseconds (50-150)
for search strings of 2 chars 200 milliseconds (190-260)
There will be no additional space used for table, but each index will take up to 200Mb (for 1 million barcodes)
PAY ATTENTION
Tested on a Microsoft SQL Server Express (64-bit) and Microsoft SQL Server Enterprise (64-bit) the optimizer of the latter is slight better but the main difference is that:
on express edition you have to extract ONLY the primary key when searching your string, if you add other columns in the SELECT, the optimizer will not use indexes anymore but it will go for full clustered index scan so you will need something like
;with
k as (-- extract only primary key
select N from like_test
where 1=0
OR ((barcode = #s) and (#l=6))
OR ((barcode like #s+'%') and (#l<6))
or ((BC2 like #s+'%') and (#l<6))
or ((BC3 like #s+'%') and (#l<5))
or ((BC4 like #s+'%') and (#l<4))
or ((BC5 like #s+'%') and (#l<3))
)
select N
from like_test t
where exists (select 1 from k where k.n = t.n)
on standard (enterprise) edition you HAVE to go for
select * from like_test -- take a look at the star
where 1=0
OR ((barcode = #s) and (#l=6))
OR ((barcode like #s+'%') and (#l<6))
or ((BC2 like #s+'%') and (#l<6))
or ((BC3 like #s+'%') and (#l<5))
or ((BC4 like #s+'%') and (#l<4))
or ((BC5 like #s+'%') and (#l<3))
You do not include many constraints, which means you want to search for string in a string -- and if there was a way to optimized an index to search a string in a string, it would be just built in!
Other things that make it hard to give a specific answer:
It's not clear what "huge" and "too long" mean.
It's not clear as to how your application works. Are you searching in batch as you add a 1,000 new products? Are you allowing a user to enter a partial barcode in a search box?
I can make some suggestions that may or may not be helpful in your case.
Speed up some of the queries
I have a database with lots of licence plates; sometimes an officer wants to search by the last 3-characters of the plate. To support this I store the license plate in reverse, then use LIKE ('ZYX%') to match ABCXYZ. When doing the search, they have the option of a 'contains' search (like you have) which is slow, or an option of doing 'Begins/Ends with' which is super because of the index. This would solve your problem some of the time (which may be good enough), especially if this is a common need.
Parallel Queries
An index works because it organizes data, an index cannot help with a string within a string because there is no organization. Speed seems to be your focus of optimization, so you could store/query your data in a way that searches in parallel. Example: if it takes 10-seconds to sequentially search 10-million rows, then having 10-parallel processes (so process searches 1-million) will take you from 10-seconds to 1-second (kind'a-sort'a). Think of it as scaling out. There are various options for this, within your single SQL Instance (try data partitioning) or across multiple SQL Servers (if that's an option).
BONUS: If you're not on a RAID setup, that can help with reads since it's a effectively of reading in parallel.
Reduce a bottleneck
One reason searching "huge" datasets take "too long" is because all that data needs to be read from the disk, which is always slow. You can skip-the-disk, and use InMemory Tables. Since "huge" isn't defined, this may not work.
UPDATED:
We know from that FULL-TEXT searches can be used for the following:
Full-Text Search -
MSDN
One or more specific words or phrases (simple term)
A word or a phrase where the words begin with specified text (prefix term)
Inflectional forms of a specific word (generation term)
A word or phrase close to another word or phrase (proximity term)
Synonymous forms of a specific word (thesaurus)
Words or phrases using weighted values (weighted term)
Are any of these fulfilled by your query requirements? If you are having to search for patterns as you described, without an consistent pattern (such as '1%'), then there may not be a way for SQL to use a SARG.
You could use Boolean statements
Coming from a C++ perspective, B-Trees are accessed from Pre-Order, In-Order, and Post-Order traversals and utilize Boolean statements to search the B-Tree. Processed much faster than string comparisons, booleans offer at the least an improved performance.
We can see this in the following two options:
PATINDEX
Only if your column is not numeric, as PATINDEX is designed for strings.
Returns an integer (like CHARINDEX) which is easier to process than strings.
CHARINDEX is a solution
CHARINDEX has no problem searching INTs and again, returns a number.
May require some extra cases built in (i.e. first number is always ignored), but you can add them like so: CHARINDEX('200', barcode) > 1.
Proof of what I am saying, let us go back to the old [AdventureWorks2012].[Production].[TransactionHistory]. We have TransactionID which contains the number of the items we want, and lets for fun assume you want every transactionID that has 200 at the end.
-- WITH LIKE
SELECT TOP 1000 [TransactionID]
,[ProductID]
,[ReferenceOrderID]
,[ReferenceOrderLineID]
,[TransactionDate]
,[TransactionType]
,[Quantity]
,[ActualCost]
,[ModifiedDate]
FROM [AdventureWorks2012].[Production].[TransactionHistory]
WHERE TransactionID LIKE '%200'
-- WITH CHARINDEX(<delimiter>, <column>) > 3
SELECT TOP 1000 [TransactionID]
,[ProductID]
,[ReferenceOrderID]
,[ReferenceOrderLineID]
,[TransactionDate]
,[TransactionType]
,[Quantity]
,[ActualCost]
,[ModifiedDate]
FROM [AdventureWorks2012].[Production].[TransactionHistory]
WHERE CHARINDEX('200', TransactionID) > 3
Note CHARINDEX removes the value 200200 in the search, so you may need to adjust your code appropriately. But look at the results:
Clearly, booleans and numbers are faster comparisons.
LIKE uses string comparisons, which again is much slower to process.
I was a bit surprised at the size of the difference, but the fundamentals are the same. Integers and Boolean statements are always faster to process than string comparisons.
I'm late to the game but here's another way to get a full-text like index in the spirit of #MtwStark's second answer.
This is a solution using a search table join
drop table if exists #numbers
select top 10000 row_number() over(order by t1.number) as n
into #numbers
from master..spt_values t1
cross join master..spt_values t2
drop table if exists [like_test]
create TABLE [like_test](
[N] INT IDENTITY(1,1) not null,
[barcode] [varchar](40) not null,
constraint pk_liketest primary key ([N])
)
insert into dbo.like_test (barcode)
select top (1000000) replace(convert(varchar(40), NEWID()), '-', '') barcode
from #numbers t,#numbers t2
drop table if exists barcodesearch
select distinct ps.n, trim(substring(ps.barcode,ty.n,100)) as searchstring
into barcodesearch
from like_test ps
inner join #numbers ty on ty.n < 40
where len(ps.barcode) > ty.n
create clustered index idx_barcode_search_index on barcodesearch (searchstring)
The final search should look like this:
declare #s varchar(20) = 'D34F15'
select distinct lt.* from dbo.like_test lt
inner join barcodesearch bs on bs.N = lt.N
where bs.searchstring like #s+'%'
If you have the option of full-text searching, you can speed this up even further by adding the full-text search column directly to the barcode table
drop table if exists #liketestupdates
select n, string_agg(searchstring, ' ')
within group (order by reverse(searchstring)) as searchstring
into #liketestupdates
from barcodesearch
group by n
alter table dbo.like_test add search_column varchar(559)
update lt
set search_column = searchstring
from like_test lt
inner join #liketestupdates lu on lu.n = lt.n
CREATE FULLTEXT CATALOG ftcatalog as default;
create fulltext index on dbo.like_test ( search_column )
key index pk_liketest
The final full-text search would look like this:
declare #s varchar(20) = 'D34F15'
set #s = '"*' + #s + '*"'
select n,barcode from dbo.like_test where contains(search_column, #s)
I understand that Estimated Costs aren't the best measure of expected performance but the number's aren't wildly off here.
With the search table join, the Estimated Subtree Cost is 2.13
With the full-text search, the Estimated Subtree Cost is 0.008
Full-text is aimed for bigger texts, let's say texts with more than about 100 chars. You can use LIKE '%string%'. (However it depends how the barcode column is defined.) Do you have an index for barcode? If not, then create one and it will improve your query.
First make the index on column on which you have to put as where clause .
Secondly for the datatype of the column which are used in where clause make them as Char in place of Varchar which will save you some space,in the table and in the indexes that will include that column.
varchar(1) column needs one more byte over char(1)
Do pull only the number of columns you need try to avoid * , be specific to number of columns you wish to select.
Don't write as
select * from products
In place of it write as
Select Col1, Col2 from products with (Nolock)

SQL Select statement loop on one column in different table

Good day Guys, Would you help me with my SQL Query. I have on proj in the web which I called INQUIRY good thing is I can store the log file of what data is being search to my project which they enter to my inquiry search box.
This is the table of Keyword have been searched in INQUIRY:
This Code :
Insert into #temptable
Select CaseNo from tblcrew
where Lastname like '%FABIANA%'
and firstname like '%MARLON%'
Insert into #temptable
Select CaseNo from tblcrew
where Lastname like '%DE JOAN%'
and firstname like '%ROLANDO%'
Insert into #temptable
Select CaseNo from tblcrew
where Lastname like '%ROSAS%'
and firstname like '%FRANCASIO%'
I want to repeat my query until all the rows in table of keyword is being search and save the result of each query into a temporary table. Is there a possibility to do that without typing all the value of in the columns of keyword.
Please anyone help me.. thanks!
All you need is join the two tables together without typing any values.
Insert into #temptable
Select c.CaseNo
from tblcrew c
inner join tblKeyword k
on c.Lastname like '%'+k.Lastname+'%'
and c.firstname like '%'+k.firstname +'%'
Usually start with the Adventure Works database for examples like this. I will be talking about exact matches with leverage an index seek, in-exact matches that leverage a index scan, and full text indexing in which you can do a in-exact match resulting in a seek.
The Person.Person table has both last and first name like your example. I keep just the primary key on business id and create one index on (last, first).
--
-- Just PK & One index for test
--
-- Sample database
use [AdventureWorks2012];
go
-- add the index
CREATE NONCLUSTERED INDEX [IX_Person_LastName_FirstName] ON [Person].[Person]
(
[LastName] ASC,
[FirstName] ASC
);
go
Run with wild card for inexact match. Run with just text for exact match. I randomly picked two names from the Person.Person table.
--
-- Run for match type
--
-- Sample database
use [AdventureWorks2012];
go
-- remove temp table
drop table #inquiry;
go
-- A table with first, last name combos to search
create table #inquiry
(
first_name varchar(50),
last_name varchar(50)
);
go
-- Add two person.person names
insert into #inquiry values
('%Cristian%', '%Petculescu%'),
('%John%', '%Kane%');
/*
('Cristian', 'Petculescu'),
('John', 'Kane');
*/
go
-- Show search values
select * from #inquiry;
go
The next step when examining run times is to clear the procedure cache and memory buffers. You do not want existing plans or data skew the numbers.
-- Remove clean buffers & clear plan cache
CHECKPOINT
DBCC DROPCLEANBUFFERS
DBCC FREEPROCCACHE
GO
-- Show time & i/o
SET STATISTICS TIME ON
SET STATISTICS IO ON
GO
The first SQL statement will do a inner join between the temporary search values table and Person.Person.
-- Exact match
select *
from
[Person].[Person] p join #inquiry i
on p.FirstName = i.first_name and p.LastName = i.last_name
The statistics and run times.
Table 'Person'. Scan count 2, logical reads 16, physical reads 8, CPU time = 0 ms, elapsed time = 29 ms.
The resulting query plan does a table scan of the #inquiry table and a index seek of the index on a last and first name. It is a nice simple plan.
Lets retry this with a inexact match using wild cards and the LIKE operator.
-- In-Exact match
select *
from
[Person].[Person] p join #inquiry i
on p.FirstName like i.first_name and p.LastName like i.last_name
The statistics and run times.
Table 'Person'. Scan count 2, logical reads 219, CPU time = 32 ms, elapsed time = 58 ms.
The resulting query plan is a-lot more complicated. We are still doing a table scan of #inquiry since it does not have an index. However, there are a-lot of nested joins going on to used the index with a impartial match.
We added three more operators to the query and the execution time is twice that of the exact match.
In short, if you are doing inexact matches with the LIKE command, they will be more expensive.
If you are searching hundreds of thousands of records, use a FULL TEXT INDEX (FTI). I wrote two articles on this topic.
http://craftydba.com/?p=1421
http://craftydba.com/?p=1629
Every night, you will have to have a process that updates the FTI with any changes. After that one hit, you can use the CONTAINS() operator to leverage the index in fuzzy matches.
I hope I explained the differences. I have seen continued confusion on this topic and I wanted to put something out on Stack Overflow that I could reference.
Best of luck Juan.

LIKE vs CONTAINS on SQL Server

Which one of the following queries is faster (LIKE vs CONTAINS)?
SELECT * FROM table WHERE Column LIKE '%test%';
or
SELECT * FROM table WHERE Contains(Column, "test");
The second (assuming you means CONTAINS, and actually put it in a valid query) should be faster, because it can use some form of index (in this case, a full text index). Of course, this form of query is only available if the column is in a full text index. If it isn't, then only the first form is available.
The first query, using LIKE, will be unable to use an index, since it starts with a wildcard, so will always require a full table scan.
The CONTAINS query should be:
SELECT * FROM table WHERE CONTAINS(Column, 'test');
Having run both queries on a SQL Server 2012 instance, I can confirm the first query was fastest in my case.
The query with the LIKE keyword showed a clustered index scan.
The CONTAINS also had a clustered index scan with additional operators for the full text match and a merge join.
I think that CONTAINS took longer and used Merge because you had a dash("-") in your query adventure-works.com.
The dash is a break word so the CONTAINS searched the full-text index for adventure and than it searched for works.com and merged the results.
Also try changing from this:
SELECT * FROM table WHERE Contains(Column, "test") > 0;
To this:
SELECT * FROM table WHERE Contains(Column, '"*test*"') > 0;
The former will find records with values like "this is a test" and "a test-case is the plan".
The latter will also find records with values like "i am testing this" and "this is the greatest".
I didn't understand actually what is going on with "Contains" keyword. I set a full text index on a column. I run some queries on the table.
Like returns 450.518 rows but contains not and like's result is correct
SELECT COL FROM TBL WHERE COL LIKE '%41%' --450.518 rows
SELECT COL FROM TBL WHERE CONTAINS(COL,N'41') ---40 rows
SELECT COL FROM TBL WHERE CONTAINS(COL,N'"*41*"') -- 220.364 rows

weird execution plan of a SQL Server query

Context: SQL Server 2008. There are 2 tables to inner join.
The fact table, which has 40 million rows, contains the patient key and the medications administered and other facts. There is a unique index (nonclustered) on medication key and patient key combined in that order.
The dimension table is the medication list (70 rows).
The join is to get the medication code (business code) based on medication key (surrogate key).
Query:
SELECT a.PKey, a.SomeFact, b.MCode
FROM tblFact a
JOIN tblDIM b ON a.MKey = b.MKey
All the columns returned are integer.
The above query runs in 7 minutes and its execution plan shows the index on (MKey,PKey) is used. The index was rebuilt right before the run.
When I disabled the index on the fact table (or copy data to a new table with same structure but without index), the same query takes only 1:40 minutes.
IO Statistics are also stunning.
With index: Table 'tblFACT'. Scan count 70, logical reads 190296338, physical reads 685138, read-ahead reads 98713
Without index: Table 'tblFACT_copy'. Scan count 17, logical reads 468891, physical reads 0, read-ahead reads 419768
Question: why does it try to use the index and head down the inefficient path?
You need to add SomeFact as an INCLUDE on the tblFact index to make it covering.
Currently, the table will be accessed twice: once for the index and then again for a lookup to get SomeFact either as a RID or key lookup (depends on if there is a clustered index)
This doesn't apply to tblDIM because I assume that MKey is the clustered index which makes it covering implicitly
In rare cases, the database chooses an incorrect execution plan. In this case, the index is used for the join, but since all data is fetched from both tables, it would be faster to just scan the whole table.
The indexed version will be much faster if you add a WHERE clause to the query, because without indexes it will still need to scan the whole table, instead of grabbing just the handful of records it needs.
There may be directives to encourage the database not to use indexes or use different indexes, but I don't know SQL server that well.
Are your statistics up to date? Check with:
SELECT object_name = Object_Name(ind.object_id)
, IndexName = ind.name
, StatisticsDate = STATS_DATE(ind.object_id, ind.index_id)
FROM SYS.INDEXES ind
order by
STATS_DATE(ind.object_id, ind.index_id) desc
Update with:
exec sp_updatestats;

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