SELECT FROM inner query slowdown - sql

We have two very similar queries, one takes 22 seconds the other takes 6 seconds. Both use an inner select, have the exact same outer columns and outer joins. The only difference is the inner select that the outer query is using to join in on.
The inner query when run alone executes in 100ms or less in both cases and returns the EXACT SAME data.
Both queries as a whole have a lot of room for improvement, but this particular oddity is really puzzling to us and we just want to understand why. To me it would seem the inner query should be executed once in 100ms then the outer stuff happens. I have a feeling the inner select may be executed multiple times.
Query that takes 6 seconds:
SELECT {whole bunch of column names}
FROM (
SELECT projectItems.* FROM projectItems
WHERE projectItems.isActive = 1
ORDER BY projectItemsID ASC
OFFSET 0 ROWS FETCH NEXT 1 ROWS ONLY
) projectItems
LEFT JOIN categories
ON projectItems.fk_category = categories.categoryID
...{more joins}
Query that takes 22 seconds:
SELECT {whole bunch of column names}
FROM (
SELECT projectItems.* FROM projectItems
WHERE projectItems.isActive = 1
AND projectItemsID = 6539
) projectItems
LEFT JOIN categories
ON projectItems.fk_category = categories.categoryID
...{more joins}

For every row in your projectItems table, in the second function, you search two columns instead of one. If projectItemsID isn't the primary key or if it isn't indexed, it takes longer to parse an extra column.'
If you look at the sizes of the tables and the number of rows each query returns, you can calculate how many comparisons need to be made for each of the queries.

I believe that you're right that the inner query is being run for every single row that is being left joined with categories.
I can't find a proper source on it right now, but you can easily test this by doing something like this and comparing the run times. Here, we can at least be sure that the inner query is only running one time. (sorry if any syntax is incorrect, but you'll get the general idea):
DECLARE #innerQuery TABLE ( [all inner query columns here] )
INSERT INTO #innerQuery
SELECT projectItems.* FROM projectItems
WHERE projectItems.isActive = 1
AND projectItemsID = 6539
SELECT {whole bunch of field names}
FROM #innerQuery as IQ
LEFT JOIN categories
ON IQ.fk_category = categories.categoryID
...{more joins}

Related

Query with Left outer join and group by returning duplicates

To begin with, I have a table in my db that is fed with SalesForce info. When I run this example query it returns 2 rows:
select * from SalesForce_INT_Account__c where ID_SAP_BAYER__c = '3783513'
When I run this next query on the same table I obtain one of the rows, which is what I need:
SELECT MAX(ID_SAP_BAYER__c) FROM SalesForce_INT_Account__c where ID_SAP_BAYER__c = '3783513' GROUP BY ID_SAP_BAYER__c
Now, I have another table (PedidosEspecialesZarateCabeceras) which has a field (NroClienteDireccionEntrega) that I can match with the field I've been using in the SalesForce table (ID_SAP_BAYER__c). This table has a key that consists of just 1 field (NroPedido).
What I need to do is join these 2 tables to obtain a row from PedidosEspecialesZarateCabeceras with additional fields coming from the SalesForce table, and in case those additional fields are not available, they should come as NULL values, so for that im using a LEFT OUTER JOIN.
The problem is, since I have to match NroClienteDireccionEntrega and ID_SAP_BAYER__c and there's 2 rows in the salesforce table with the same ID_SAP_BAYER__c, my query returns 2 duplicate rows from PedidosEspecialesZarateCabeceras (They both have the same NroPedido).
This is an example query that returns duplicates:
SELECT
cab.CUIT AS CUIT,
convert(nvarchar(4000), cab.NroPedido) AS NroPedido,
sales.BillingCity__c as Localidad,
sales.BillingState__c as IdProvincia,
sales.BillingState__c_Desc as Provincia,
sales.BillingStreet__c as Calle,
sales.Billing_Department__c as Distrito,
sales.Name as RazonSocial,
cab.NroCliente as ClienteId
FROM PedidosEspecialesZarateCabeceras AS cab WITH (NOLOCK)
LEFT OUTER JOIN
SalesForce_INT_Account__c AS sales WITH (NOLOCK) ON
cab.NroClienteDireccionEntrega = sales.ID_SAP_BAYER__c
and sales.ID_SAP_BAYER__c in
( SELECT MAX(ID_SAP_BAYER__c)
FROM SalesForce_INT_Account__c
GROUP BY ID_SAP_BAYER__c
)
WHERE cab.NroPedido ='5320'
Even though the join has MAX and Group By, this returns 2 duplicate rows with different SalesForce information (Because of the 2 salesforce rows with the same ID_SAP_BAYER__c), which should not be possible.
What I need is for the left outer join in my query to pick only ONE of the salesforce rows to prevent duplication like its happening right now. For some reason the select max with the group by is not working.
Maybe I should try to join this tables in a different way, can anyone give me some other ideas on how to join the two tables to return just 1 row? It doesnt matter if the SalesForce row that gets picked out of the 2 isn't the correct one, I just need it to pick one of them.
Your IN clause is not actually doing anything, since...
SELECT MAX(ID_SAP_BAYER__c)
FROM SalesForce_INT_Account__c
GROUP BY ID_SAP_BAYER__c
... returns all possible IDSAP_BAYER__c values. (The GROUP BY says you want to return one row per unique ID_SAP_BAYER__c and then, since your MAX is operating on exactly one unique value per group, you simply return that value.)
You will want to change your query to operate on a value that is actually different between the two rows you are trying to differentiate (probably the MAX(ID) for the relevant ID_SAP_BAYER__c). Plus, you will want to link that inner query to your outer query.
You could probably do something like:
...
LEFT OUTER JOIN
SalesForce_INT_Account__c sales
ON cab.NroClienteDireccionEntrega = sales.ID_SAP_BAYER__c
and sales.ID in
(
SELECT MAX(ID)
FROM SalesForce_INT_Account__c sales2
WHERE sales2.ID_SAP_BAYER__c = cab.NroClienteDireccionEntrega
)
WHERE cab.NroPedido ='5320'
By using sales.ID in ... SELECT MAX(ID) ... instead of sales.ID_SAP_BAYER__c in ... SELECT MAX(ID_SAP_BAYER__c) ... this ensures you only match one of the two rows for that ID_SAP_BAYER__c. The WHERE sales2.ID_SAP_BAYER__c = cab.NroClienteDireccionEntrega condition links the inner query to the outer query.
There are multiple ways of doing the above, especially if you don't care which of the relevant rows you match on. You can use the above as a starting point and make it match your preferred style.
An alternative might be to use OUTER APPLY with TOP 1. Something like:
SELECT
...
FROM PedidosEspecialesZarateCabeceras AS cab
OUTER APPLY(
SELECT TOP 1 *
FROM SalesForce_INT_Account__c s1
WHERE cab.NroClienteDireccionEntrega = s1.ID_SAP_BAYER__c
) sales
WHERE cab.NroPedido ='5320'
Without an ORDER BY the match that TOP 1 chooses will be arbitrary, but I think that's what you want anyway. (If not, you could add an ORDER BY).

Speeding up a query with INNER JOIN

I have a query that takes a long time to execute. I've waited for about 10 mins and it's still not finished executing.
The query looks something like this:
SELECT
one.ID,
two.NAME,
two.STATUS,
four.KEY,
four.VALUE,
count(one.ID) as num
FROM TABLE_ONE one, TABLE_TWO two, TABLE_THREE three, TABLE_FOUR four
WHERE one.STATE='RED'
AND (two.STATUS='ON' OR two.STATUS='OFF')
AND (
four.KEY='FINAL'
OR four.KEY='LIMIT'
OR (
four.KEY='MODE'
AND (
four.VALUE='T'
OR four.VALUE='R')))
GROUP BY one.ID, two.NAME, two.STATUS, four.KEY, four.VALUE
ORDER BY group_name ASC;
I have another query which is equivalent but executes very fast (about 1 second to execute).
Here is that query:
SELECT
one.ID,
two.NAME,
two.STATUS,
four.KEY,
four.VALUE,
count(one.ID) as num
FROM TABLE_ONE one
INNER JOIN TABLE_TWO two
ON one.ID=two.ID
INNER JOIN TABLE_THREE three
ON two.ID=three.GROUP_ID
INNER JOIN TABLE_FOUR four
ON three.ID=four.ID
WHERE one.STATE='RED'
AND (two.STATUS='ON' OR two.STATUS='OFF')
AND (
four.KEY='FINAL'
OR four.KEY='LIMIT'
OR (
four.KEY='MODE'
AND (
four.VALUE='T'
OR four.VALUE='R')))
GROUP BY one.ID, two.NAME, two.STATUS, four.KEY, four.VALUE
ORDER BY group_name ASC;
I'm kind of confused why the query with INNER JOIN executes really fast (about 1 second) and the one without takes a long time (waited about 10mins and still not finised executing).
Is there anything I can do to the query without the INNER JOIN to speed up the execution time?
I am using ORACLE.
In the first query, the tables are not really joined on any columns. The result is called cross join. Cross join between two table returns rows equals to number of rows in the first table times the numbers of rows in the second table.
Inner join joins based on given set of columns.
Your long running query has no join conditions to relate one table to the other. Therefore it is creating a cartesian product of all the records in each table. So if each table has 10 rows, it would generate 10*10*10*10=10,000 result rows before performing the aggregate functions. Larger tables just get worse. If each table had 1,000 rows you'd end up generating 1,000,000,000,000 rows.
Your faster query has join criteria which significantly reduces the number of rows in the result set, which is why it is more performant.
Lets say you have N values for ID. In the first query you will create N * N * N * N (or N ^ 4) rows.
In the second you will create N rows.
In big O notation:
O(N^4)
vs
O(N)
Now you have a real world example of the impact.

query behave not as expected

I have a query:
select count(*) as total
from sheet_record right join
(select * from sheet_record limit 10) as sr
on 1=1;
If i understood correct (which i think i did not), right join suppose to return all row from right table in conjunction with left table. it suppose to be at list 10 row. But query returns only 1 row with 1 column 'total' . And it doesn't matter left full inner join it will be, result is the same always.
If i reverse tables and use left join with small modification of query, then it work correct (Modifications have no matter because in this case i get exactly what i expected to get). But I am interested to find what i actually didn't understand about join and why this query works not as expected.
You are returning one column because the select contains an aggregation function, turning this into an aggregation query. The query should be returning 10 times the number of rows in the sheet_record table.
Your query is effectively a cross join. So, if you did:
select *
from sheet_record right join
(select * from sheet_record limit 10) as sr
on 1=1;
You would get 10 rows for each record in sheet_record. Each of those records would have additional columns from one of ten records from the same table.
You are using a count(*) function, without any groupings. This will pretty much will result in retrieving a single row back. Try running your query without the count() to see if you get something closer to what you expect.
Eventually with help of commentators I did understood what was wrong. Not wrong actually, but what exactly i was not catching.
// this code below is work fine. query will return page 15 with 10 records in.
select *from sheet_record inner join (select count(*) as total from sheet_record) as sr on 1=1 limit 10 offset 140;
I was thinking that join takes table from left and join with the right table. But the moment i was working on script(above) I had on right side a view(table built by subquery) instead of pure table and i was thinking that left side as well a view, made by (select * from sheet_record) which is a mistake.
Idea is to get set of records from table X with additional column having value of total number of records in table.
(This is common problem when there is a demand to show table in UI using paging. To know how many pages still should be available i need to know how many record in total so i can calculate how many pages still available)
I think it should be something
select * from (
(here is some subquery which will give a view using count(*) function on some table X and it will be used as left table)
right join
(here is some subquery which will get some set or records from table X with limit and offset)
on 1=1 //becouse i need all row from right table(view) in all cases it should be true
)
Query with right join will a bit complicated.
I am using postgres.
So eventually i managed to get result with right join
select * from (select count(*) as total from sheet_record) as srt right join (select * from sheet_record limit 10 offset 140) as sr on 1=1;

SQL Performance: SELECT DISTINCT versus GROUP BY

I have been trying to improve query times for an existing Oracle database-driven application that has been running a little sluggish. The application executes several large queries, such as the one below, which can take over an hour to run. Replacing the DISTINCT with a GROUP BY clause in the query below shrank execution time from 100 minutes to 10 seconds. My understanding was that SELECT DISTINCT and GROUP BY operated in pretty much the same way. Why such a huge disparity between execution times? What is the difference in how the query is executed on the back-end? Is there ever a situation where SELECT DISTINCT runs faster?
Note: In the following query, WHERE TASK_INVENTORY_STEP.STEP_TYPE = 'TYPE A' represents just one of a number of ways that results can be filtered. This example was provided to show the reasoning for joining all of the tables that do not have columns included in the SELECT and would result in about a tenth of all available data
SQL using DISTINCT:
SELECT DISTINCT
ITEMS.ITEM_ID,
ITEMS.ITEM_CODE,
ITEMS.ITEMTYPE,
ITEM_TRANSACTIONS.STATUS,
(SELECT COUNT(PKID)
FROM ITEM_PARENTS
WHERE PARENT_ITEM_ID = ITEMS.ITEM_ID
) AS CHILD_COUNT
FROM
ITEMS
INNER JOIN ITEM_TRANSACTIONS
ON ITEMS.ITEM_ID = ITEM_TRANSACTIONS.ITEM_ID
AND ITEM_TRANSACTIONS.FLAG = 1
LEFT OUTER JOIN ITEM_METADATA
ON ITEMS.ITEM_ID = ITEM_METADATA.ITEM_ID
LEFT OUTER JOIN JOB_INVENTORY
ON ITEMS.ITEM_ID = JOB_INVENTORY.ITEM_ID
LEFT OUTER JOIN JOB_TASK_INVENTORY
ON JOB_INVENTORY.JOB_ITEM_ID = JOB_TASK_INVENTORY.JOB_ITEM_ID
LEFT OUTER JOIN JOB_TASKS
ON JOB_TASK_INVENTORY.TASKID = JOB_TASKS.TASKID
LEFT OUTER JOIN JOBS
ON JOB_TASKS.JOB_ID = JOBS.JOB_ID
LEFT OUTER JOIN TASK_INVENTORY_STEP
ON JOB_INVENTORY.JOB_ITEM_ID = TASK_INVENTORY_STEP.JOB_ITEM_ID
LEFT OUTER JOIN TASK_STEP_INFORMATION
ON TASK_INVENTORY_STEP.JOB_ITEM_ID = TASK_STEP_INFORMATION.JOB_ITEM_ID
WHERE
TASK_INVENTORY_STEP.STEP_TYPE = 'TYPE A'
ORDER BY
ITEMS.ITEM_CODE
SQL using GROUP BY:
SELECT
ITEMS.ITEM_ID,
ITEMS.ITEM_CODE,
ITEMS.ITEMTYPE,
ITEM_TRANSACTIONS.STATUS,
(SELECT COUNT(PKID)
FROM ITEM_PARENTS
WHERE PARENT_ITEM_ID = ITEMS.ITEM_ID
) AS CHILD_COUNT
FROM
ITEMS
INNER JOIN ITEM_TRANSACTIONS
ON ITEMS.ITEM_ID = ITEM_TRANSACTIONS.ITEM_ID
AND ITEM_TRANSACTIONS.FLAG = 1
LEFT OUTER JOIN ITEM_METADATA
ON ITEMS.ITEM_ID = ITEM_METADATA.ITEM_ID
LEFT OUTER JOIN JOB_INVENTORY
ON ITEMS.ITEM_ID = JOB_INVENTORY.ITEM_ID
LEFT OUTER JOIN JOB_TASK_INVENTORY
ON JOB_INVENTORY.JOB_ITEM_ID = JOB_TASK_INVENTORY.JOB_ITEM_ID
LEFT OUTER JOIN JOB_TASKS
ON JOB_TASK_INVENTORY.TASKID = JOB_TASKS.TASKID
LEFT OUTER JOIN JOBS
ON JOB_TASKS.JOB_ID = JOBS.JOB_ID
LEFT OUTER JOIN TASK_INVENTORY_STEP
ON JOB_INVENTORY.JOB_ITEM_ID = TASK_INVENTORY_STEP.JOB_ITEM_ID
LEFT OUTER JOIN TASK_STEP_INFORMATION
ON TASK_INVENTORY_STEP.JOB_ITEM_ID = TASK_STEP_INFORMATION.JOB_ITEM_ID
WHERE
TASK_INVENTORY_STEP.STEP_TYPE = 'TYPE A'
GROUP BY
ITEMS.ITEM_ID,
ITEMS.ITEM_CODE,
ITEMS.ITEMTYPE,
ITEM_TRANSACTIONS.STATUS
ORDER BY
ITEMS.ITEM_CODE
Here is the Oracle query plan for the query using DISTINCT:
Here is the Oracle query plan for the query using GROUP BY:
The performance difference is probably due to the execution of the subquery in the SELECT clause. I am guessing that it is re-executing this query for every row before the distinct. For the group by, it would execute once after the group by.
Try replacing it with a join, instead:
select . . .,
parentcnt
from . . . left outer join
(SELECT PARENT_ITEM_ID, COUNT(PKID) as parentcnt
FROM ITEM_PARENTS
) p
on items.item_id = p.parent_item_id
I'm fairly sure that GROUP BY and DISTINCT have roughly the same execution plan.
The difference here since we have to guess (since we don't have the explain plans) is IMO that the inline subquery gets executed AFTER the GROUP BY but BEFORE the DISTINCT.
So if your query returns 1M rows and gets aggregated to 1k rows:
The GROUP BY query would have run the subquery 1000 times,
Whereas the DISTINCT query would have run the subquery 1000000 times.
The tkprof explain plan would help demonstrate this hypothesis.
While we're discussing this, I think it's important to note that the way the query is written is misleading both to the reader and to the optimizer: you obviously want to find all rows from item/item_transactions that have a TASK_INVENTORY_STEP.STEP_TYPE with a value of "TYPE A".
IMO your query would have a better plan and would be more easily readable if written like this:
SELECT ITEMS.ITEM_ID,
ITEMS.ITEM_CODE,
ITEMS.ITEMTYPE,
ITEM_TRANSACTIONS.STATUS,
(SELECT COUNT(PKID)
FROM ITEM_PARENTS
WHERE PARENT_ITEM_ID = ITEMS.ITEM_ID) AS CHILD_COUNT
FROM ITEMS
JOIN ITEM_TRANSACTIONS
ON ITEMS.ITEM_ID = ITEM_TRANSACTIONS.ITEM_ID
AND ITEM_TRANSACTIONS.FLAG = 1
WHERE EXISTS (SELECT NULL
FROM JOB_INVENTORY
JOIN TASK_INVENTORY_STEP
ON JOB_INVENTORY.JOB_ITEM_ID=TASK_INVENTORY_STEP.JOB_ITEM_ID
WHERE TASK_INVENTORY_STEP.STEP_TYPE = 'TYPE A'
AND ITEMS.ITEM_ID = JOB_INVENTORY.ITEM_ID)
In many cases, a DISTINCT can be a sign that the query is not written properly (because a good query shouldn't return duplicates).
Note also that 4 tables are not used in your original select.
The first thing that should be noted is the use of Distinct indicates a code smell, aka anti-pattern. It generally means that there is a missing join or an extra join that is generating duplicate data. Looking at your query above, I am guessing that the reason why group by is faster (without seeing the query), is that the location of the group by reduces the number of records that end up being returned. Whereas distinct is blowing out the result set and doing row by row comparisons.
Update to approach
Sorry, I should have been more clear. Records are generated when
users perform certain tasks in the system, so there is no schedule. A
user could generate a single record in a day or hundreds per-hour. The
important things is that each time a user runs a search, up-to-date
records must be returned, which makes me doubtful that a materialized
view would work here, especially if the query populating it would take
long to run.
I do believe this is the exact reason to use a materialized view. So the process would work this way. You take the long running query as the piece that builds out your materialized view, since we know the user only cares about "new" data after they perform some arbitrary task in the system. So what you want to do is query against this base materialized view, which can be refreshed constantly on the back-end, the persistence strategy involved should not choke out the materialized view (persisting a few hundred records at a time won't crush anything). What this will allow is Oracle to grab a read lock (note we don't care how many sources read our data, we only care about writers). In the worst case a user will have "stale" data for microseconds, so unless this is a financial trading system on Wall Street or a system for a nuclear reactor, these "blips" should go unnoticed by even the most eagle eyed users.
Code example of how to do this:
create materialized view dept_mv FOR UPDATE as select * from dept;
Now the key to this is as long as you don' t invoke refresh you won't lose any of the persisted data. It will be up to you to determine when you want to "base line" your materialized view again (midnight perhaps?)
You should use GROUP BY to apply aggregate operators to each group and DISTINCT if you only need to remove duplicates.
I think the performance is the same.
In your case i think you should use GROUP BY.

LEFT INNER JOIN vs. LEFT OUTER JOIN - Why does the OUTER take longer?

We have the query below. Using a LEFT OUTER join takes 9 seconds to execute. Changing the LEFT OUTER to an LEFT INNER reduces the execution time to 2 seconds, and the same number of rows are returned. Since the same number of rows from the dbo.Accepts table are being processed, regardless of the join type, why would the outer take 3x longer?
SELECT CONVERT(varchar, a.ReadTime, 101) as ReadDate,
a.SubID,
a.PlantID,
a.Unit as UnitID,
a.SubAssembly,
m.Lot
FROM dbo.Accepts a WITH (NOLOCK)
LEFT OUTER Join dbo.Marker m WITH (NOLOCK) ON m.SubID = a.SubID
WHERE a.LastModifiedTime BETWEEN #LastModifiedTimeStart AND #LastModifiedTimeEnd
AND a.SubAssembly = '400'
The fact that the same number of rows is returned is an after fact, the query optimizer cannot know in advance that every row in Accepts has a matching row in Marker, can it?
If you join two tables A and B, say A has 1 million rows and B has 1 row. If you say A LEFT INNER JOIN B it means only rows that match both A and B can result, so the query plan is free to scan B first, then use an index to do a range scan in A, and perhaps return 10 rows. But if you say A LEFT OUTER JOIN B then at least all rows in A have to be returned, so the plan must scan everything in A no matter what it finds in B. By using an OUTER join you are eliminating one possible optimization.
If you do know that every row in Accepts will have a match in Marker, then why not declare a foreign key to enforce this? The optimizer will see the constraint, and if is trusted, will take it into account in the plan.
1) in a query window in SQL Server Management Studio, run the command:
SET SHOWPLAN_ALL ON
2) run your slow query
3) your query will not run, but the execution plan will be returned. store this output
4) run your fast version of the query
5) your query will not run, but the execution plan will be returned. store this output
6) compare the slow query version output to the fast query version output.
7) if you still don't know why one is slower, post both outputs in your question (edit it) and someone here can help from there.
This is because the LEFT OUTER Join is doing more work than an INNER Join BEFORE sending the results back.
The Inner Join looks for all records where the ON statement is true (So when it creates a new table, it only puts in records that match the m.SubID = a.SubID). Then it compares those results to your WHERE statement (Your last modified time).
The Left Outer Join...Takes all of the records in your first table. If the ON statement is not true (m.SubID does not equal a.SubID), it simply NULLS the values in the second table's column for that recordset.
The reason you get the same number of results at the end is probably coincidence due to the WHERE clause that happens AFTER all of the copying of records.
Join (SQL) Wikipedia
Wait -- did you actually mean that "the same number of rows ... are being processed" or that "the same number of rows are being returned"? In general, the outer join would process many more rows, including those for which there is no match, even if it returns the same number of records.