In a SQL query I have to join many tables and its very expensive for the DB.
In the DB a hostgroup has many host, there are like 20 hostgroups, and there is 4 hostgroups that I don't use...
I was wandering if I add a "not in" operator in my query, excluding those 4 hostgroup, the query will be less expensive? or just make thing worst using more resources on the db?
this is my query, just in case...
select history.clock, hstgrp.name as hostgroup, hstgrp.groupid as hgid , hosts.name as hostname ,
items.name as item, hosts.hostid, history.value as porcentaje, items.key_ as key ,items.itemid,
applications.name as appname, applications.applicationid as appid
FROM history
join items_applications on history.itemid = items_applications.itemid
join applications on items_applications.applicationid = applications.applicationid
join items on items.itemid = history.itemid
join hosts on items.hostid = hosts.hostid
join hosts_groups on hosts.hostid = hosts_groups.hostid
join hstgrp on hosts_groups.groupid = hstgrp.groupid
where lower(items.name) SIMILAR TO lower('Used disk space%|Used disk space on%')
and hstgrp.name not in ('Discovered', 'Discover VMs') <==============
The additional filter sure cannot harm, but unless it is very selective, it will probably not reduce the execution time significantly.
I am reduced to guessing, since you didn't add EXPLAIN (ANALYZE, BUFFERS) output to the question, but I'd assume that the query returns a lot of rows and is bound to be slow.
You could change the SIMILAR TO condition to
WHERE lower(items.name) LIKE lower('Used disk space%')
and support it with an index:
CREATE INDEX ON items (lower(name) text_pattern_ops);
Perhaps that will speed up the execution somewhat.
Related
I have something of a Search App. There are 7 fields (first name, last name, phone, street, city, shop number, credit card number) where user can write parameters and it's gonna find him clients in the database. Everything is working with AND condition, so when first name is 'Andy' and last name is 'Larkin' is only gonna find Andy Larkins etc. User can leave a field empty, that means when first name is 'Andy' then it should find all the Andys etc. Database looks like this:
The 'Relation' table is to connect person and a shop. Person must have 1 address, 1 shop, can have multiple addresses, multiple shops and no credit card/multiple credit cards. Now, I have to handle all the filtering in a single query, I can't check some conditions before and then construct the query another way, I just don't have that option.
When I search by first name or last name it's fast (both in Person table), but when I search by phone number, or credit card number - it's taking a lot of time. There is a lot of data in the database, but still, my query is bad, I'm not really good at writing queries, especially in Oracle. Here's the query:
SELECT
PERSON.personId,
PERSON.firstName,
PERSON.lastName
ADDRESS.street,
ADDRESS.city,
ADDRESS.phoneNumber
FROM
PERSON
LEFT JOIN ADDRESS ON PERSON.personId = ADDRESS.personId,
LEFT JOIN RELATION ON PERSON.personId = RELATION.personId,
LEFT JOIN SHOPS ON RELATION.shopId = SHOPS.shopId
LEFT JOIN CREDITCARDS ON PERSON.personId = CREDITCARDS.personId
WHERE
PERSON.firstName = NVL(?, PERSON.firstName),
PERSON.lastName = NVL(?, PERSON.lastName),
ADDRESS.phoneNumber = NVL(?, ADDRESS.phoneNumber),
ADDRESS.street = NVL(?, ADDRESS.street),
ADDRESS.city = NVL(?, ADDRESS.city),
SHOPS.shopNumber = NVL(?, SHOPS.shopNumber),
CREDITCARDS.creditCardNumber = NVL(?, CREDITCARDS.creditCardNumber);
The parameters that user left empty are passed as NULLS, that's why I use NVL. When I delete all conditions and leave let's say a credit card number, then it's fast, so I guess that means that all the unnecessary condition checking is slowing the query, and I don't really need that condition checking in most cases, it's just there in case a user passes something.
If I would have the option to check for conditions and only then construct a query then I would just add the conditions that are needed, but I don't have that option. I was thinking about adding some 'IFs' in the query, but I'm not sure that's even possible, all I could find was 'IF/CASE WHEN' but couldn't find any examples that apply to my case. I also tried this:
...WHERE (? IS NULL OR (PERSON.firstName = NVL(?, PERSON.firstName))) AND...
That didn't help, and I got tons of duplicated (different only in address or something - person can have multiple addresses) results (even with 'DISTINCT').
It's not homework, that database is huge with lot of other fields, but I simplified it here, there is also a lot of data there. Thanks for help.
A few things to think about here.
Be careful about queries that might not make sense; such as those that query a credit card number and an address. Queries of that nature fall into a fan trap.
Creating referential integrity constraints in the database will allow the optimizer to do join elimination.
It would be much better for the optimizer, if you could build the query "where clause" dynamically, rather than using NVL functions.
A nested select on the shops might improve performance especially considering it's outer joined. The query below should be enough to get you the idea.
Regarding de-duplication- it's hard because you are selecting Id's and 'distinct' won't help. You'd probably have to use the group by syntax and that might slow the query even more.
If sorting can be done on the client it might help with performance. If the amount of data being returned is significant due to fan-out of relational data and group by isn't a good option then creating a stored procedure might be the best option so most the work is done on database and minimal data over the wire.
SELECT
p.personId,
p.firstName,
a.city,
a.phoneNumber,
shop.shopNumber
FROM
PERSON p,
ADDRESS a,
CREDITCARDS c,
(select ss.personId, ss.shopId, ss.shopNumber from shop s, relation r
where s.shopId = r.shopId) as shop
WHERE
p.personId = a.personId AND
p.personId = c.personId AND
p.personId = shop.personId (+)
I have this very old and SLOW query that I am trying to optimize, but I am not sure I can do anything to it, but add more indexes on columns involved in WHERE, JOIN and ORDER BY.
Query:
SELECT TOP 400 jobticket.jobnumber, jobticket.typeform, jobticket.filename, jobticket.req_number, jobticket.reqd_del_date, jobticket.point_of_contact, jobticket.status, jobticket.DapsDate, jobticket.elpod, job_info.IDOrderMaskedStatus, job_info.job_status, job_info.SalesID, job_info.location, job_info.TOMetadataID
FROM jobticket WITH (NOLOCK)
INNER JOIN job_info WITH (NOLOCK) ON job_info.jobnumber = jobticket.jobnumber
WHERE
(
NOT(
(jobticket.status = 'Complete' OR jobticket.status = 'Completed')
and (job_info.job_status = 'Actualized' OR job_info.job_status = ''
OR job_info.job_status = 'Actualized Credit Billed'
OR job_info.job_status = 'DWAS Actualized' OR job_info.job_status = 'DWAS Actualized Credit Billed'
)
)
or
((SELECT COUNT(job_status) AS Expr1 FROM tblConsolidatedBilling AS tblConsolidatedBilling_1 WITH (NOLOCK)
WHERE (job_status <> 'Actualized'
AND job_status <> 'Actualized Credit Billed')
AND (master_jobnumber = jobticket.jobnumber)) > 0)
)
and (jobticket.status != 'Waiting Approval' or (jobticket.status = 'Waiting Approval' and jobticket.DPGType is null))
and jobticket.typeform <> 'todpg'
and ((job_info.isHidden <> 1 or job_info.isHidden is null) and job_info.isInConcurrentRelease is null)
and job_info.deleted != '1'
and jobticket.status != 'New Job'
and jobticket.status != 'PRFYCLSFD'
ORDER BY
job_info.expediencyLevel DESC,
jobticket.jobnumber DESC
Execution Plan:
In all honesty I don't know what to do with this query.
Should I add individual nonclustered indexes on all columns involved in WHERE JOIN and ORDER BY?
There are many indexes on these tables, but I am not sure whether they are helpful in this query:
Looking at this SQL, I don't really see any clear criteria that is being used to fetch the rows. It looks like it's just excluding a lot of rows with different criteria. My guess is that the tickets usually end up in a state where most of the rows are, and those are not included in the results?
The problem with this is, that it doesn't really have any clear criteria for that, and it has a lot of different rules there, so that's why it ends up doing a clustered index scan + key lookups for all the rows. The scan starts from jobinfo, but I'm not sure if it would make any difference if it would start from jobticket.
Removing most of the indexes is probably a good place to start, but it won't speed up the select at all.
The query looks quite complex, so my guess is that you can't create an index view that would contain this data. That might help assuming this query is executed often and data is not changed that much (and the overhead of maintaining huge number of indexes would have been removed), but this might not be possible.
Another idea would be to investigate the rules when the rows can be excluded, and is there a possibility have more clear rules for that, so it could be indexed, maybe by adding a persisted computed column into the table.
You haven't mentioned how long this actually takes, and how many rows there are in the tables, so everything is basically just a guess. Including more data + statistics io output into the question might help.
ps. I don't personally recommend using NOLOCK except in really special cases, because it can cause problems that are really hard to solve, like reading the same data more than once or skipping rows totally.
A simple fix would be to make the indexes on job_info and tblConsolidatedBilling covering because a ton of time is spent in key lookups there. That should give an integer factor speedup. If that's not enough we need to investigate further.
This is the SQL with CommonTableExpression. Note, that USERS_PROJECTS_CTE used twice.
WITH USERS_PROJECTS_CTE (PRO_ID, SHOW_IAS, USERNAME)
AS
(
SELECT up.PRO_ID, up.SHOW_IAS, ISNULL(u.FIRST_NAME, '') + ' ' + ISNULL(u.SECOND_NAME, '')
FROM SFMIS07_PRO.USERS_PROJECTS up
INNER JOIN SFMIS07_ADM.USERS AS u
ON up.USER_ID = u.ID
WHERE up.IS_RESP_PERSON = 1 AND up.valid_to is null
)
SELECT up.PRO_ID,
up1.USERNAME as RESP_USER1,
up2.USERNAME as RESP_USER2,
up.COUNT_
FROM SFMIS07_PRO.PRO_RESP_USERS_KERNEL_MV AS up
LEFT JOIN USERS_PROJECTS_CTE AS up1 ON up.PRO_ID = up1.PRO_ID AND up1.SHOW_IAS=1
LEFT JOIN USERS_PROJECTS_CTE AS up2 ON up.PRO_ID = up2.PRO_ID AND up2.SHOW_IAS=0
The Execution Plan. Note that CTE displayed twice:
Questions:
am I right that CTE is not only displayed twice but processed twice?
is it possible to inform QO to reuse CTE ?
is it possible for QO in principle to detect "the same SQL fragment" and reuse results (I imagine the realization of this - by coping already prepared data)?
how to optimize the query (without using temporal tables :) ?
Am I right that CTE is not only displayed twice but processed twice?
Yes
Is it possible to inform QO to reuse CTE ?
Not directly but there are some hacks to encourage this.
is it possible for QO in principle to detect "the same SQL fragment"
and reuse results (I imagine the realization of this - by coping
already prepared data)?
In principle yes. See Microsoft Research Paper Efficient Exploitation of Similar Subexpressions for Query
Processing for examples.
how to optimize the query (without using temporal tables :) ?
The most reliable way would be to use a temporary (not temporal) table. See Provide a hint to force intermediate materialization of CTEs or derived tables for a more hacky workaround.
I've been banging my head on this issue for a little while now, and decided I should ask for help. I have a table which holds temperature/humidity chart recorder data (currently over 775,000 records) from which I am trying to run a statistical query against it. The problem is that this often will take up to two minutes, and sometimes will not come back at all - causing me to force close the program (Control-Alt-Delete). At first, I didn't have as much of a problem - it was only after I hit the magical 500k records mark that I started getting serious slowdowns, getting progressively worse as more data was compiled and imported into the table.
Here is the query (pass-through):
SELECT dbo.tblRecorderLogs.strAreaAssigned, Min(dbo.tblRecorderLogs.datDateRecorded) AS FirstRecorderDate, Max(dbo.tblRecorderLogs.datDateRecorded) AS LastRecordedDate,
Round(Avg(dbo.tblRecorderLogs.intTempCelsius),2) AS AverageTempC,
Round(Avg(dbo.tblRecorderLogs.intRHRecorded),2) AS AverageRH,
Count(dbo.tblRecorderLogs.strAreaAssigned) AS Records
FROM dbo.tblRecorderLogs
GROUP BY dbo.tblRecorderLogs.strAreaAssigned
ORDER BY dbo.tblRecorderLogs.strAreaAssigned;
Here is the table structure in which the chart data is stored:
idRecorderDataID Number Primary Key
datDateEntered Date/Time (indexed, duplicates OK)
datTimeEntered Date/Time
intTempCelcius Number
intDewPointCelcius Number
intWetBulbCelcius Number
intMixingGPP Number
intRHRecorded Number
strAssetRecorder Text (indexed, duplicates OK)
strAreaAssigned Text (indexed, duplicates OK)
I am trying to write a program which will allow people to pull data from this table based on Area Assigned, as well as start and end dates. With the dataset size I currently have, this kind of report is simply too much for it to handle (it seems) and the machine doesn't ever return an answer. I've had to extend the ODBC timeout to almost 180 seconds in any queries dealing with this table, simply because of the size. I could use some serious help, if people have some. Thank you in advance!
-- Edited 08/13/2012 # 1050 hours --
I have not been able to test the query on the SQL Server due to the fact that the IT department has taken control of the machine in question, and has someone logged into it full-time using the remote management console. I have tried an interim step to lessen the impact of the performance issue, but I am still looking for a permanent solution to this issue.
Interim step:
I created a local table mirroring the structure of the dbo.tblRecorderLogs SQL Server table, to which I do a INSERT INTO using the former SELECT statement as it's subquery. Then any subsequent statistical analysis is drawn from this 'temporary' local table. After the process is complete, the local table is truncated.
-- Edited 08/13/2012 # 1217 hours --
Ran the shown query on the SQL Server Management Console, took 1 minute 38 seconds to complete according to the query timer provided by the console.
-- Edit 08/15/2012 # 1531 hours --
Tried to run query as VBA DoCmd.RunSQL statement to populate a temporary table using the following code:
INSERT INTO tblTempRecorderDataStatsByArea ( strAreaAssigned, datFirstRecord,
datLastRecord, intAveTempC, intAveRH, intRecordCount )
SELECT dbo_tblRecorderLogs.strAreaAssigned, Min(dbo_tblRecorderLogs.datDateRecorded)
AS MinOfdatDateRecorded, Max(dbo_tblRecorderLogs.datDateRecorded) AS MaxOfdatDateRecorded,
Round(Avg(dbo_tblRecorderLogs.intTempCelsius),2) AS AveTempC,
Round(Avg(dbo_tblRecorderLogs.intRHRecorded),2) AS AveRHRecorded,
Count(dbo_tblRecorderLogs.strAreaAssigned) AS CountOfstrAreaAssigned FROM
dbo_tblRecorderLogs GROUP BY dbo_tblRecorderLogs.strAreaAssigned ORDER BY
dbo_tblRecorderLogs.strAreaAssigned
The problem arises when the code is executed, the query takes so long - it encounters Timeout before it finishes. Still hoping for a 'magic bullet' to fix this...
-- Edited 08/20/2012 # 1241 hours --
The only 'quasi' solution I've found is running the failed query repeatedly (sort of priming the pump, as it were) so that when the query is called again by my program - it has a relative chance of actually completing before the ODBC SQL Server driver times out. Basically, a filthy filthy hack - but I don't have a better one to combat this issue.
I've tried creating a view, which works on the server side - but doesn't speed things up.
The proper fields being aggregated are indexed properly, so I can't make any changes there.
I am only pulling information from the database that is immediately useful to user - no 'SELECT * madness' going on here.
I think I am, officially, out of things to try - aside from throwing raw computing horsepower at the problem, which isn't a solution right now as the item isn't live, and I have no budget to procure better hardware. I will post this as an 'answer' and leave it up until Sept 3rd - where if I do not have better answers, I will accept my own answer and accept defeat.
When I've had to run min/max functions on several fields from the same table I've often found it quicker to do each column separately as a subquery in the from line of the main/outer query.
So your query would be like this:
SELECT rLogs1.strAreaAssigned, rLogs1.FirstRecorderDate, rLogs2.LastRecorderDate, rLog3.AverageTempC, rLogs4.AverageRH, rLogs5.Records
FROM (((
(SELECT strAreaAssigned, min(datDateRecorded) as FirstRecorderDate FROM dbo.tblRecorderLogs GROUP BY strAreaAssigned) rLogs1
inner join
(SELECT strAreaAssigned, Max(datDateRecorded) as LastRecordedDate, FROM dbo.tblRecorderLogs GROUP BY strAreaAssigned) rLogs2
on rLogs1.strAreaAssigned = rLogs2.strAreaAssigned)
inner join
(SELECT strAreaAssigned, Round(Avg(intTempCelsius),2) AS AverageTempC, FROM dbo.tblRecorderLogs GROUP BY strAreaAssigned) rLogs3
on rLogs1.strAreaAssigned = rLogs3.strAreaAssigned)
inner join
(SELECT strAreaAssigned, Round(Avg(intRHRecorded),2) AS AverageRH, FROM dbo.tblRecorderLogs GROUP BY strAreaAssigned) rLogs4
on rLogs1.strAreaAssigned = rLogs4.strAreaAssigned)
inner join
(SELECT strAreaAssigned, Count(strAreaAssigned) AS Records, FROM dbo.tblRecorderLogs GROUP BY strAreaAssigned) rLogs5
on rLogs1.strAreaAssigned = rLogs5.strAreaAssigned
ORDER BY rLogs1.strAreaAssigned;
If you take your query and the one above, copy them into the same query window in SQL Server and run the estimated execution plan you should be able to compare them and see which one works better.
I'm using SQL Server 2008. I have a table with over 3 million records, which is related to another table with a million records.
I have spent a few days experimenting with different ways of querying these tables. I have it down to two radically different queries, both of which take 6s to execute on my laptop.
The first query uses a brute force method of evaluating possibly likely matches, and removes incorrect matches via aggregate summation calculations.
The second gets all possibly likely matches, then removes incorrect matches via an EXCEPT query that uses two dedicated indexes to find the low and high mismatches.
Logically, one would expect the brute force to be slow and the indexes one to be fast. Not so. And I have experimented heavily with indexes until I got the best speed.
Further, the brute force query doesn't require as many indexes, which means that technically it would yield better overall system performance.
Below are the two execution plans. If you can't see them, please let me know and I'll re-post then in landscape orientation / mail them to you.
Brute-force query:
SELECT ProductID, [Rank]
FROM (
SELECT p.ProductID, ptr.[Rank], SUM(CASE
WHEN p.ParamLo < si.LowMin OR
p.ParamHi > si.HiMax THEN 1
ELSE 0
END) AS Fail
FROM dbo.SearchItemsGet(#SearchID, NULL) AS si
JOIN dbo.ProductDefs AS pd
ON pd.ParamTypeID = si.ParamTypeID
JOIN dbo.Params AS p
ON p.ProductDefID = pd.ProductDefID
JOIN dbo.ProductTypesResultsGet(#SearchID) AS ptr
ON ptr.ProductTypeID = pd.ProductTypeID
WHERE si.Mode IN (1, 2)
GROUP BY p.ProductID, ptr.[Rank]
) AS t
WHERE t.Fail = 0
Index-based exception query:
with si AS (
SELECT DISTINCT pd.ProductDefID, si.LowMin, si.HiMax
FROM dbo.SearchItemsGet(#SearchID, NULL) AS si
JOIN dbo.ProductDefs AS pd
ON pd.ParamTypeID = si.ParamTypeID
JOIN dbo.ProductTypesResultsGet(#SearchID) AS ptr
ON ptr.ProductTypeID = pd.ProductTypeID
WHERE si.Mode IN (1, 2)
)
SELECT p.ProductID
FROM dbo.Params AS p
JOIN si
ON si.ProductDefID = p.ProductDefID
EXCEPT
SELECT p.ProductID
FROM dbo.Params AS p
JOIN si
ON si.ProductDefID = p.ProductDefID
WHERE p.ParamLo < si.LowMin OR p.ParamHi > si.HiMax
My question is, based on the execution plans, which one look more efficient? I realize that thing may change as my data grows.
EDIT:
I have updated the indexes, and now have the following execution plan for the second query:
Trust the optimizer.
Write the query that most simply expresses what you're trying to achieve. If you're having perfomance problems with that query, then you should look at whether there are any missing indexes. But you still shouldn't have to explicitly work with these indexes.
Don't concern yourself by considerations of how you might implement such a search.
In very rare circumstances, you may need to further force the query to use particular indexes (via hints), but this is probably < 0.1% of queries.
In your posted plans, your "optimized" version is causing scans against 2 indexes of your (I presume) Params table (PK_Params_1, IX_Params_1). Without seeing the queries, it's difficult to know why this is happening, but if you're comparing against having a single scan against a table ("Brute force") and two, it's easy to see why the second isn't more efficient.
I think I'd try:
SELECT p.ProductID, ptr.[Rank]
FROM dbo.SearchItemsGet(#SearchID, NULL) AS si
JOIN dbo.ProductDefs AS pd
ON pd.ParamTypeID = si.ParamTypeID
JOIN dbo.Params AS p
ON p.ProductDefID = pd.ProductDefID
JOIN dbo.ProductTypesResultsGet(#SearchID) AS ptr
ON ptr.ProductTypeID = pd.ProductTypeID
LEFT JOIN Params p_anti
on p_anti.ProductDefId = pd.ProductDefID and
(p_anti.ParamLo < si.LowMin or p_anti.ParamHi > si.HiMax)
WHERE si.Mode IN (1, 2)
AND p_anti.ProductID is null
GROUP BY p.ProductID, ptr.[Rank]
I.e. introduce an anti-join that eliminates the results you don't want.
In SQL Server Management Studio, put both queries in the same query window and get the query plan for both at once. It should determine the query plans for both and give you a 'percent of total batch' for each one. The query with the lower percent of the total batch will be the better performing one.
Does 6 seconds on a laptop = .006 seconds on productions hardware? The part of your queries which worry me are the clustered index scans shown in the query plan. In my experience any time a query plan includes a CI scan it means the query will only get slower when data is added to the table.
What do the two functions yield as it appears they are the cause of the table scans? Is it possible to persist the data in the db and update the LoMin and HiMax as rows are added.
Looking at the two execution plans neither is very good. Look how far to the left the wide lines are. The wide lines means there are many rows. We need to reduce the number of rows earlier in the process so we do not work with such large hash tables and large sorts and nested loops.
BTW how many rows does your source have and how many rows are included in the result set?
Thank you all for your input and help.
From reading what you wrote, experimenting, and digging into the execution plan, I discovered the answer is tipping point.
There were too many records being returned to warrant use of the index.
See here (Kimberly Tripp).