SQL Server - Speed up count on large table - sql

I have a table with close to 30 million records. Just several columns. One of the column 'Born' have not more than 30 different values and there is an index defined on it. I need to be able to filter on that column and efficiently page through results.
For now I have (example if the year I'm searching for is '1970' - it is a parameter in my stored procedure):
WITH PersonSubset as
(
SELECT *, ROW_NUMBER() OVER (ORDER BY Born asc) AS Row
FROM Person WITH (INDEX(IX_Person_Born))
WHERE Born = '1970'
)
SELECT *, (SELECT count(*) FROM PersonSubset) AS TotalPeople
FROM PersonSubset
WHERE Row BETWEEN 0 AND 30
Every query of that sort (only Born parameter used) returns just over 1 million results.
I've noticed the biggest overhead is on the count used to return the total results. If I remove (SELECT count(*) FROM PersonSubset) AS TotalPeople from the select clause the whole thing speeds up a lot.
Is there a way to speed up the count in that query. What I care about is to have the paged results returned and the total count.

Updated following discussion in comments
The cause of the problem here is very low cardinality of the IX_Person_Born index.
SQL indexes are very good at quickly narrowing down values, but they have problems when you have lots of records with the same value.
You can think of it as like the index of a phone book - if you want to find "Smith, John" you first find that there are lots of names that begin with S, and then pages and pages of people called Smith, and then lots of Johns. You end up scanning the book.
This is compounded because the index in the phone book is clustered - the records are sorted by surname. If instead you want to find everyone called "John" you'll be doing a lot of looking up.
Here there are 30 million records but only 30 different values, which means that the best possible index is still returning around 1 million records - at that sort of scale it might as well be a table-scan. Each of those 1 million results is not the actual record - it's a lookup from the index to the table (the page number in the phone book analogy), which makes it even slower.
A high cardinality index (say for full date of birth), rather than year would be much quicker.
This is a general problem for all OLTP relational databases: low cardinality + huge datasets = slow queries because index-trees don't help much.
In short: there's no significantly quicker way to get the count using T-SQL and indexes.
You have a couple of options:
1. Data Aggregation
Either OLAP/Cube rollups or do it yourself:
select Born, count(*)
from Person
group by Born
The pro is that cube lookups or checking your cache is very fast. The problem is that the data will get out of date and you need some way to account for that.
2. Parallel Queries
Split into two queries:
SELECT count(*)
FROM Person
WHERE Born = '1970'
SELECT TOP 30 *
FROM Person
WHERE Born = '1970'
Then run these either in parallel server side, or add it to the user interface.
3. No-SQL
This problem is one of the big advantages no-SQL solutions have over traditional relational databases. In a no-SQL system the Person table is federated (or sharded) across lots of cheap servers. When a user searches every server is checked at the same time.
At this point a technology change is probably out, but it may be worth investigating so I've included it.
I have had similar problems in the past with databases of this kind of size, and (depending on context) I've used both options 1 and 2. If the total here is for paging then I'd probably go with option 2 and AJAX call to get the count.

DECLARE #TotalPeople int
--does this query run fast enough? If not, there is no hope for a combo query.
SET #TotalPeople = (SELECT count(*) FROM Person WHERE Born = '1970')
WITH PersonSubset as
(
SELECT *, ROW_NUMBER() OVER (ORDER BY Born asc) AS Row
FROM Person WITH (INDEX(IX_Person_Born))
WHERE Born = '1970'
)
SELECT *, #TotalPeople as TotalPeople
FROM PersonSubset
WHERE Row BETWEEN 0 AND 30
You usually can't take a slow query, combine it with a fast query, and wind up with a fast query.
One of the column 'Born' have not more than 30 different values and there is an index defined on it.
Either SQL Server isn't using the index or statistics, or the index and statistics aren't helpful enough.
Here is a desperate measure that will force Sql's hand (at the potential cost of making writes very expensive - measure that, and blocking schema changes to the Person table while the view exists).
CREATE VIEW dbo.BornCounts WITH SCHEMABINDING
AS
SELECT Born, COUNT_BIG(*) as NumRows
FROM dbo.Person
GROUP BY Born
GO
CREATE UNIQUE CLUSTERED INDEX BornCountsIndex ON BornCounts(Born)
By putting a clustered index on a view, you make it a system maintained copy. The size of this copy is much smaller than 30 Million rows, and it has the exact information you're looking for. I did not have to change the query to get it to use the view, but you're free to use the view's name in the query if you like.

WITH PersonSubset as
(
SELECT *, ROW_NUMBER() OVER (ORDER BY Born asc) AS Row
FROM Person WITH (INDEX(IX_Person_Born))
WHERE Born = '1970'
)
SELECT *, **max(Row) AS TotalPeople**
FROM PersonSubset
WHERE Row BETWEEN 0 AND 30
why not like that ?
edit , dont know why bold doesnt work :<

Here is a novel approach using system dmv's if you can get by with a "good enough" count, you don't mind creating an index for every distinct value for [Born], and you don't mind feeling a little bit dirty inside.
Create a filtered index for each year:
--pick a column to index, it doesn't matter which.
CREATE INDEX IX_Person_filt_1970 on Person ( id ) WHERE Born = '1970'
CREATE INDEX IX_Person_filt_1971 on Person ( id ) WHERE Born = '1971'
CREATE INDEX IX_Person_filt_1972 on Person ( id ) WHERE Born = '1972'
Then use the [rows] column from sys.partitions to to get a rowcount.
WITH PersonSubset as
(
SELECT *, ROW_NUMBER() OVER (ORDER BY Born asc) AS Row
FROM Person WITH (INDEX(IX_Person_Born))
WHERE Born = '1970'
)
SELECT *,
(
SELECT sum(rows)
FROM sys.partitions p
inner join sys.indexes i on p.object_id = i.object_id and p.index_id =i.index_id
inner join sys.tables t on t.object_id = i.object_id
WHERE t.name ='Person'
and i.name = 'IX_Person_filt_' + '1970' --or at #p1
) AS TotalPeople
FROM PersonSubset
WHERE Row BETWEEN 0 AND 30
Sys.partitions isn't guaranteed to be accurate in 100% of cases (usually it is exact or really close) This approach won't work if you need to filter on anything but [Born]

Related

Any suggestions to speed up slow geography query?

We have a table of Customers, with each one's location as a Geography column, and a table of Branch Offices also with each one's location as a Geography column (we populate the Geography columns from latitude and longitude columns)
We need to run a query (view) that's intended to show the closest branch office to each customer, based on Geography columns, and it runs fine with a couple of thousand customers. We just received a big job that needs to run with 700,000 customers and it takes hours to run. Can anyone suggest any ways to speed up this SQL?
WITH CLOSEST AS (
SELECT *, ROW_NUMBER()
OVER (
PARTITION BY CustNum
ORDER BY Miles
) AS RowNo
FROM
(
SELECT
CustNum,
BranchNum,
CONVERT(DECIMAL(10, 6), (BranchLoc.STDistance(CustLoc)) / 1609.344) AS Miles
FROM
Branch_Locations
CROSS JOIN
Cust_Locations
) AS T
)
SELECT TOP 100 PERCENT CustNum, BranchNum, Miles, RowNo FROM CLOSEST WHERE RowNo = 1 ORDER BY CustNum, MILES
Could there be a way to put the distance comparison into the JOIN? Nothing comes to mind so far...
Thanks for any suggestions!
So, what you're doing here is calculating the distance from each point to each other point, then ranking. SQL Server Spatial is actually set up in such a way that this is entirely unnecessary.
The first thing you want to do is make a spatial index on each table; documentation on how to do this can be found here. Don't worry too much about the specific paramters here, while you can definitely improve performance by adjusting them, having a spatial index at all will drastically improve performance.
The second thing you want to do is to make sure the spatial index is being used; documentation on how to make sure this happens can be found here. Make sure that you filter out any null spatial information!
So, what this has told as so far is a way to take a point and find the closest point in another long list of tables; but this is SQL Server, we want to to this set based!
My recommendation is to use a little a priori knowledge and write a query using that.
WITH CLOSEST AS (
SELECT
C.CustNum,
B.BranchNum,
ROW_NUMBER() OVER (PARTITION BY C.CustNum ORDER BY B.BranchLoc.STDistance(C.CustLoc)/1609.344 ASC) AS Miles
FROM
Branch_Locations B
INNER JOIN
Cust_Locations C
ON
B.BranchLoc.STDistance(C.CustLoc)/1609.344 < 100 --100 miles as a maximum search distance is a reasonable number to me
WHERE
B.BranchLoc IS NOT NULL
AND C.CustLoc IS NOT NULL
) AS T
SELECT
CustNum,
BranchNum,
Miles,
RowNo
FROM
CLOSEST
WHERE
RowNo = 1
ORDER BY
CustNum,
MILES
There are other techniques that you can use, such as my response here, however at the end of the day the most important takeaway is to create spatial indexes and make sure they are used.

Paginated UNION ALL result - best performance

We have a situation where we need results from 4 different tables combined into one list and paginate it through OFFSET/FETCH.
What want to select records from tables a, b, c & d, order them by CreatedDatetime and then OFFSET X, FETCH Y. Tables are quite big (in terms of numbers of rows) and it sounds horrible to do just UNION ALL and then pagination because it would mean probably compiling whole list of records and then taking paginated part.
Problem is that none of the tables can be taken as reference to extract Start/End Datetime window because every collection might but also might not contain records from any of the table. For example, ending result might contain records from any combination of tables a; a/b; a/b/c; a/b/c/d; b; b/c;.... and we need fixed size number to be returned (paging size, for example, being 20).
Any ideas on how to most effectively approach this?
UPDATE
Based on question from #HABO
There are unfortunately no special clues like that about queries. We are showing user activities in the system. There are different kinds of it (tables we select over). Now, query pops up data for administrator who views the activities. How administrator will look at data may vary drastically: some users will have thousands of activities in last few hours and admin will want to see them all. In other cases, users will have 3 actions in a day and admin will see just first page of data.
PS. It's not a pure log tables as activities act as state machines over time, each having their states, which we also look for in these queries.
if you know the page size (eg 100) then you can simply write 4 Top 100 queries (order by Create Date) - Then do a Union ALL on the result.
That way even if all the first 100 records come from 1 table you are covered.
For Subsequent Paging queries - You'll need to record the last displayed row from each table and use this as your High-Water mark for the next fetch - (Select top 100 FROM TableA Where RowID > #HighWater)
Should be fairly efficient...
This is where a cache comes in useful. You can either cache the result of the query in your application layer and do the paging there if it is not too large, or cache the results of the query in a table (or temp table) if it is large.
There would be filters i suppose. From what you say, those may vary a lot. So at the worst scenario, all columns can be filters.
My suggestion is to use 5 views, one for each table and a final one union them. Just make sure all filter columns go up the physical tables as straightforward as possible.
Finally, select the master view and fetch but be careful of the order by clause. Make sure order by has unique data combination else you might have cases where a row change pages on a simple plain refresh. If there is user order by defined, force add some key columns at the end.
How to safely ensure order by to have distinct values for 100% safe fetch/offset:
At the 4 views create a new column with a simple constant number as value, e.g. 1, 2, 3, 4 AS [TableSource]
Make sure you select the PK of each table. If you don't have, you have to create one in the views, probably using ROW_NUMBER or NEWID, as [Pk] for example.
Finally, when selecting from the master view, you ORDER BY CreateDate, Pk, TableSource. This way you are 100% safe that within the same set of data any row will be placed exactly at the same position, resulting correct paging.
Example of safely isolating a page of 30 rows order by CreateDate:
SELECT * FROM (
SELECT src, id, ROW_NUMBER() OVER(ORDER BY dt DESC,src,id)rn FROM (
SELECT 1 src, id, dt FROM table1 /*WHERE x=y*/ UNION ALL
SELECT 2 src, id, dt FROM table2 /*WHERE x=y*/ UNION ALL
SELECT 3 src, id, dt FROM table3 /*WHERE x=y*/ UNION ALL
SELECT 4 src, id, dt FROM table4 /*WHERE x=y*/)alltables
)data WHERE data.rn BETWEEN 3001 AND 3030

Creating a denormalized table from a normalized key-value table using 100s of joins

I have an ETL process which takes values from an input table which is a key value table with each row having a field ID and turning it into a more denormalized table where each row has all the values. Specifically, this is the input table:
StudentFieldValues (
FieldId INT NOT NULL,
StudentId INT NOT NULL,
Day DATE NOT NULL,
Value FLOAT NULL
)
FieldId is a foreign key from table Field, Day is a foreign key from table Days. The PK is the first 3 fields. There are currently 188 distinct fields. The output table is along the lines of:
StudentDays (
StudentId INT NOT NULL,
Day DATE NOT NULL,
NumberOfClasses FLOAT NULL,
MinutesLateToSchool FLOAT NULL,
... -- the rest of the 188 fields
)
The PK is the first 2 fields.
Currently the query that populates the output table does a self join with StudentFieldValues 188 times, one for each field. Each join equates StudentId and Day and takes a different FieldId. Specifically:
SELECT Students.StudentId, Days.Day,
StudentFieldValues1.Value NumberOfClasses,
StudentFieldValues2.Value MinutesLateToSchool,
...
INTO StudentDays
FROM Students
CROSS JOIN Days
LEFT OUTER JOIN StudentFieldValues StudentFieldValues1
ON Students.StudentId=StudentFieldValues1.StudentId AND
Days.Day=StudentFieldValues1.Day AND
AND StudentFieldValues1.FieldId=1
LEFT OUTER JOIN StudentFieldValues StudentFieldValues2
ON Students.StudentId=StudentFieldValues2.StudentId AND
Days.Day=StudentFieldValues2.Day AND
StudentFieldValues2.FieldId=2
... -- 188 joins with StudentFieldValues table, one for each FieldId
I'm worried that this system isn't going to scale as more days, students and fields (especially fields) are added to the system. Already there are 188 joins and I keep reading that if you have a query with that number of joins you're doing something wrong. So I'm basically asking: Is this something that's gonna blow up in my face soon? Is there a better way to achieve what I'm trying to do? It's important to note that this query is minimally logged and that's something that wouldn't have been possible if I was adding the fields one after the other.
More details:
MS SQL Server 2014, 2x XEON E5 2690v2 (20 cores, 40 threads total), 128GB RAM. Windows 2008R2.
352 million rows in the input table, 18 million rows in the output table - both expected to increase over time.
Query takes 20 minutes and I'm very happy with that, but performance degrades as I add more fields.
Think about doing this using conditional aggregation:
SELECT s.StudentId, d.Day,
max(case when sfv.FieldId = 1 then sfv.Value end) as NumberOfClasses,
max(case when sfv.FieldId = 2 then sfv.Value end) as MinutesLateToSchool,
...
INTO StudentDays
FROM Students s CROSS JOIN
Days d LEFT OUTER JOIN
StudentFieldValues sfv
ON s.StudentId = sfv.StudentId AND
d.Day = sfv.Day
GROUP BY s.StudentId, d.Day;
This has the advantage of easy scalability. You can add hundreds of fields and the processing time should be comparable (longer, but comparable) to fewer fields. It is also easer to add new fields.
EDIT:
A faster version of this query would use subqueries instead of aggregation:
SELECT s.StudentId, d.Day,
(SELECT TOP 1 sfv.Value FROM StudentFieldValues WHERE sfv.FieldId = 1 and sfv.StudentId = s.StudentId and sfv.Day = sfv.Day) as NumberOfClasses,
(SELECT TOP 1 sfv.Value FROM StudentFieldValues WHERE sfv.FieldId = 2 and sfv.StudentId = s.StudentId and sfv.Day = sfv.Day) as MinutesLateToSchool,
...
INTO StudentDays
FROM Students s CROSS JOIN
Days d;
For performance, you want a composite index on StudentFieldValues(StudentId, day, FieldId, Value).
Yes, this is going to blow up. You have your definitions of "normalized" and "denormalized" backwards. The Field/Value table design is not a relational design. It's a variation of the entity-attribute-value design, which has all sorts of problems.
I recommend you do not try to pivot the data in an SQL query. It doesn't scale well that way. Instea, you need to query it as a set of rows, as it is stored in the database, and fetch back the result set into your application. There you write code to read the data row by row, and apply the "fields" to fields of an object or a hashmap or something.
I think there may be some trial and error here to see what works but here are some things you can try:
Disable indexes and re-enable after data load is complete
Disable any triggers that don't need to be ran upon data load scenarios.
The above was taken from an msdn post where someone was doing something similar to what you are.
Think about trying to only update the de-normalized table based on changed records if this is possible. Limiting the result set would be much more efficient if this is a possibility.
You could try a more threaded iterative approach in code (C#, vb, etc) to build this table by student where you aren't doing the X number of joins all at one time.

SQL Server cross join performance

I have a table that has 14,091 rows (2 columns, let's say first name, last name). I then have a calendar table that has 553 rows of just dates (first of each month). I do a cross join in order to get every combination of first name, last name, & first of month because this is my requirement. This takes just over a minute.
Is there anything I can do about this to make it faster or can a cross join never get any faster like I suspect?
People Table
first_name varchar2(100)
last_name varchar2(1000)
Dates Table
dt DateTime
select a.first_name, a.last_name, b.dt
from people a, dates b
It will be slow as it making all possible combinations. 14091 * 553. It will not going to be fast until you have either index or inner join.
Yeah. Takes over a minute. Let's get this clear. You talk of 14091 * 553 rows - that is 7792323. Rounded that is 7.8 million rows. And loading them into a data table (which is not known for performance).
Want to see slow? Put them into a grid. THEN you see slow.
The requirements make no sense in a table. None. Absolutely none.
And no, there is no way to speed up the loading of 7.8 million rows into a data structure that is not meant to hold these amounts of data.

Is a GROUP BY on UNIQUE key calculates all the groups before applying LIMIT clause?

If I GROUP BY on a unique key, and apply a LIMIT clause to the query, will all the groups be calculated before the limit is applied?
If I have hundred records in the table (each has a unique key), Will I have 100 records in the temporary table created (for the GROUP BY) before a LIMIT is applied?
A case study why I need this:
Take Stack Overflow for example.
Each query you run to show a list of questions, also shows the user who asked this question, and the number of badges he has.
So, while a user<->question is one to one, user<->badges is one has many.
The only way to do it in one query (and not one on questions and another one on users and then combine results), is to group the query by the primary key (question_id) and join+group_concat to the user_badges table.
The same goes for the questions TAGS.
Code example:
Table Questions:
question_id (int)(pk)| question_body(varchar)
Table tag-question:
question-id (int) | tag_id (int)
SELECT:
SELECT quesuestions.question_id,
questions.question_body,
GROUP-CONCAT(tag_id,' ') AS 'tags-ids'
FROM
questions
JOIN
tag_question
ON
questions.question_id=tag-question.question-id
GROUP BY
questions.question-id
LIMIT 15
Yes, the order the query executes is:
FROM
WHERE
GROUP
HAVING
SORT
SELECT
LIMIT
LIMIT is the last thing calculated, so your grouping will be just fine.
Now, looking at your rephrased question, then you're not having just one row per group, but many: in the case of stackoverflow, you'll have just one user per row, but many badges - i.e.
(uid, badge_id, etc.)
(1, 2, ...)
(1, 3, ...)
(1, 12, ...)
all those would be grouped together.
To avoid full table scan all you need are indexes. Besides that, if you need to SUM, for example, you cannot avoid a full scan.
EDIT:
You'll need something like this (look at the WHERE clause):
SELECT
quesuestions.question_id,
questions.question_body,
GROUP_CONCAT(tag_id,' ') AS 'tags_ids'
FROM
questions q1
JOIN tag_question tq
ON q1.question_id = tq.question-id
WHERE
q1.question_id IN (
SELECT
tq2.question_id
FROM
tag_question tq2
ON q2.question_id = tq2.question_id
JOIN tag t
tq2.tag_id = t.tag_id
WHERE
t.name = 'the-misterious-tag'
)
GROUP BY
q1.question_id
LIMIT 15
LIMIT does get applied after GROUP BY.
Will the temporary table be created or not, depends on how your indexes are built.
If you have an index on the grouping field and don't order by the aggregate results, then an INDEX SCAN FOR GROUP BY is applied, and each aggregate is counted on the fly.
That means that if you don't select an aggregate due to the LIMIT, it won't ever be calculated.
But if you order by an aggregate, then, of course, all of them need to be calculated before they can be sorted.
That's why they are calculated first and then the filesort is applied.
Update:
As for your query, see what EXPLAIN EXTENDED says for it.
Most probably, question_id is a PRIMARY KEY for your table, and most probably, it will be used in a scan.
That means no filesort will be applies and the join itself will not ever happen after the 15'th row.
To make sure, rewrite your query as following:
SELECT question_id,
question_body,
(
SELECT GROUP_CONCAT(tag_id, ' ')
FROM tag_question t
WHERE t.question_id = q.question_id
)
FROM questions q
ORDER BY
question_id
LIMIT 15
First, it is more readable,
Second, it is more efficient, and
Third, it will return even untagged questions (which your current query doesn't).
If the field you're grouping on is indexed, it shouldn't do a full table scan.