I have a T-SQL code that delta-copies data from the source table (SrcTable) to the destination table (DestTable). The data is inserted into the source table by multiple sessions and copied to the destination table by a SQL Server Agent job.
Here's the snippet which inserts the batch into the destination table:
...
WITH cte
AS (SELECT st.SrcTable_ID,
st.SrcTable_CreatedDateTime
FROM SrcTable st WITH (READCOMMITTEDLOCK, INDEX(PK_SrcTable))
WHERE st.SrcTable_ID
BETWEEN #FromID AND #ToID)
INSERT DestTable
(
DestTable_SrcTableID
)
SELECT cte.SrcTable_ID
FROM cte;
...
both tables are partitioned on CreatedDateTime column which default to SYSUTCDATETIME
CREATE TABLE [dbo].[SrcTable](
[SrcTable_ID] [BIGINT] IDENTITY(1,1) NOT NULL,
[SrcTable_CreatedDateTime] [DATETIME2](3) NOT NULL,
CONSTRAINT [PK_SrcTable] PRIMARY KEY CLUSTERED
(
[SrcTable_ID] ASC,
[SrcTable_CreatedDateTime] ASC
) ON [ps_Daily]([SrcTable_CreatedDateTime])
) ON [ps_Daily]([SrcTable_CreatedDateTime])
GO
CREATE TABLE [dbo].[DestTable](
[DestTable_ID] [BIGINT] IDENTITY(1,1) NOT NULL,
[DestTable_CreatedDateTime] [DATETIME2](3) NOT NULL,
[DestTable_SrcTableID] [BIGINT] NOT NULL,
CONSTRAINT [PK_DestTable] PRIMARY KEY CLUSTERED
(
[DestTable_ID] ASC,
[DestTable_CreatedDateTime] ASC
) ON [ps_Daily]([DestTable_CreatedDateTime])
) ON [ps_Daily]([DestTable_CreatedDateTime])
GO
This code has been running for years copying millions of records a day with no issues.
Recently it started missing a single row every couple of weeks.
Here's an example of such a batch with #FromID=2140 and #ToID=2566 and one missing row (2140)
SELECT * FROM dbo.SrcTable st
LEFT JOIN dbo.DestTable dt ON st.SrcTable_ID=dt.DestTable_SrcTableID
WHERE st.SrcTable_ID BETWEEN 2140 AND 2566
ORDER BY st.SrcTable_ID ASC
The only plausible explanation that I can think of is that the allocation of identity values (SrcTable_ID) happens outside of the transaction which inserts into the source table (which I learned from an excellent answer by Paul White on the related question, but judging by the time stamps in both tables this scenario seems highly unlikely.
The question is:
How likely is it that the missing row was invisible to the SELECT statement because its' identity was allocated outside of the inserting transaction and before the lock was acquired, given the fact that the next row in the batch (2141) was inserted into the source table a couple of seconds later but was successfully picked up?
We're running on Microsoft SQL Server 2019 (RTM-CU16) (KB5011644) - 15.0.4223.1 (X64)
I have an SQLite database as part of an iOS app which works fine for the most part but certain small changes to a query can result in it taking 1000x longer to complete. Here's the 2 tables I have involved:
create table "journey_item" ("id" SERIAL NOT NULL PRIMARY KEY,
"position" INTEGER NOT NULL,
"last_update" BIGINT NOT NULL,
"rank" DOUBLE PRECISION NOT NULL,
"skipped" BOOLEAN NOT NULL,
"item_id" INTEGER NOT NULL,
"journey_id" INTEGER NOT NULL);
create table "content_items" ("id" SERIAL NOT NULL PRIMARY KEY,
"full_id" VARCHAR(32) NOT NULL,
"title" VARCHAR(508),
"timestamp" BIGINT NOT NULL,
"item_size" INTEGER NOT NULL,
"http_link" VARCHAR(254),
"local_url" VARCHAR(254),
"creator_id" INTEGER NOT NULL,
"from_id" INTEGER,"location_id" INTEGER);
Tables have indexes on primary and foreign keys.
And here are 2 queries which give a good example of my problem
SELECT * FROM content_items ci
INNER JOIN journey_item ji ON ji.item_id = ci.id WHERE ji.journey_id = 1
SELECT * FROM content_items ci
LEFT OUTER JOIN journey_item ji ON ji.item_id = ci.id WHERE ji.journey_id = 1
The first query takes 167 ms to complete while the second takes 3.5 minutes and I don't know why the outer join would make such a huge difference.
Edit:
Without the WHERE part the second query only takes 267 ms
The two queries should have the same result set (the where clause turns the left join into an inner join)`. However, SQLite probably doesn't recognize this.
If you have an index on journey_item(journey_id, item_id), then this would be used for the inner join version. However, the second version is probably scanning the first table for the join. An index on journey_item(item_id) would help, but probably still not match the performance of the first query.
For a specific task I need to store the identity of a row in a tabel to access it later. Most of these tables do NOT have a numeric ID and the primary key sometimes consists of multiple fields. VARCHAR & INT combined.
Background info:
The participating tables have a trigger storing delete, update and insert events in a general 'sync' tabel (Oracle v11). Every 15 minutes a script is then launched to update corresponding tables in a remote database (SQL Server 2012).
One solution I came up with was to use multiple columns in this 'sync' table, 3 INT columns and 3 VARCHAR columns. A table with 2 VARCHAR columns would then use 2 VARCHAR columns in this 'sync' table.
A better/nicer solution would be to 'select' the value of the primary key and store this in this table.
Example:
CREATE TABLE [dbo].[Workers](
[company] [nvarchar](50) NOT NULL,
[number] [int] NOT NULL,
[name] [nvarchar](50) NOT NULL,
CONSTRAINT [PK_Workers] PRIMARY KEY CLUSTERED ( [company] ASC, [number] ASC )
)
// Fails:
SELECT [PK_Workers], [name] FROM [dbo].[Workers]
UPDATE [dbo].[Workers] SET [name]='new name' WHERE [PK_Workers]=#PKWorkers
// Bad (?) but works:
SELECT ([company] + CAST([number] AS NVARCHAR)) PK, [name] FROM [dbo].[Workers];
UPDATE [dbo].[Workers] SET [name]='newname' WHERE ([company] + CAST([number] AS NVARCHAR))=#PK
The [PK_Workers] fails in these queries. Is there another way to get this value without manually combining and casting the index?
Or is there some other way to do this that I don't know?
for each table create a function returning a concatenated primary key. create a function based index on this function too. then use this function in SELECT and WHERE clauses
Have an issue with SQL Server performance and wanted to see if anyone can give some tips about improving the performance of an update query.
What I'm doing is updating one table with data from another table. Here's some of the basics:
SQL Server 2008 R2
Data is pumped to WO table originally from other system (pumped in using datareader and sqlbulkcopy in ADO.NET)
Additional data is pumped to TEMP_REMARKS (pumped in using datareader and sqlbulkcopy in ADO.NET)
Unfortunately, combining the WO and REMARKS in the originating system (via the reader query) is not possible (mainly performance reasons)
Update to WO occurs using value from TEMP_REMARKS where two columns are updated
Note that the column being transferred from TEMP_REMARKS to REMARKS is a nvarchar(max) and is being placed into another nvarchar(max) column (actually two - see query)
WO has 4m+ records
TEMP_REMARKS has 7m+ records
For the join between the two, the following is what is being used:
/* === UPDATE THE DESCRIPTION */
UPDATE WO
SET WO_DESCRIPTION = TEMP_REMARKS.REMARKS
FROM WO
INNER JOIN TEMP_REMARKS ON WO.WO_DESCRIPTION_ID = TEMP_REMARKS.REMARKS_ID;
/* === UPDATE THE FINDINGS */
UPDATE WO
SET FINDINGS = TEMP_REMARKS.REMARKS
FROM WO
INNER JOIN TEMP_REMARKS ON WO.FINDINGS_ID = TEMP_REMARKS.REMARKS_ID;
The problem at this point is that the update to the WO table is taking over two hours to complete. I've tried using the MERGE statement with no success. I've got other more completed procedures in the db that don't take nearly as long, so I'm convinced that it is not the configuration of the SQL Server itself.
Is there something that should be done when updating nvarchar(max) columns?
What can be done to improve the performance of this query?
Here are the table definitions:
CREATE TABLE [dbo].[WO](
[DOCUMENT_ID] [decimal](18, 0) NOT NULL,
[WO_DESCRIPTION_ID] [decimal](18, 0) NULL,
[WO_DESCRIPTION] [nvarchar](max) NULL,
[FINDINGS_ID] [decimal](18, 0) NULL,
[FINDINGS] [nvarchar](max) NULL,
.... bunch of other fields
CONSTRAINT [PK_WO] PRIMARY KEY CLUSTERED
(
[DOCUMENT_ID] ASC
)
This is the table definition for the TEMP_REMARKS:
CREATE TABLE [dbo].[TEMP_REMARKS](
[REMARKS_ID] [decimal](18, 0) NOT NULL,
[REMARKS] [nvarchar](max) NULL
) ON [PRIMARY]
I think, first of all you should consider to create primary key on TEMP_REMARKS, or at least some index on REMARKS_ID
The box this query is running on is a dedicated server running in a datacenter.
AMD Opteron 1354 Quad-Core 2.20GHz
2GB of RAM
Windows Server 2008 x64 (Yes I know I only have 2GB of RAM, I'm upgrading to 8GB when the project goes live).
So I went through and created 250,000 dummy rows in a table to really stress test some queries that LINQ to SQL generates and make sure they're not to terrible and I noticed one of them was taking an absurd amount of time.
I had this query down to 17 seconds with indexes but I removed them for the sake of this answer to go from start to finish. Only indexes are Primary Keys.
Stories table --
[ID] [int] IDENTITY(1,1) NOT NULL,
[UserID] [int] NOT NULL,
[CategoryID] [int] NOT NULL,
[VoteCount] [int] NOT NULL,
[CommentCount] [int] NOT NULL,
[Title] [nvarchar](96) NOT NULL,
[Description] [nvarchar](1024) NOT NULL,
[CreatedAt] [datetime] NOT NULL,
[UniqueName] [nvarchar](96) NOT NULL,
[Url] [nvarchar](512) NOT NULL,
[LastActivityAt] [datetime] NOT NULL,
Categories table --
[ID] [int] IDENTITY(1,1) NOT NULL,
[ShortName] [nvarchar](8) NOT NULL,
[Name] [nvarchar](64) NOT NULL,
Users table --
[ID] [int] IDENTITY(1,1) NOT NULL,
[Username] [nvarchar](32) NOT NULL,
[Password] [nvarchar](64) NOT NULL,
[Email] [nvarchar](320) NOT NULL,
[CreatedAt] [datetime] NOT NULL,
[LastActivityAt] [datetime] NOT NULL,
Currently in the database there is 1 user, 1 category and 250,000 stories and I tried to run this query.
SELECT TOP(10) *
FROM Stories
INNER JOIN Categories ON Categories.ID = Stories.CategoryID
INNER JOIN Users ON Users.ID = Stories.UserID
ORDER BY Stories.LastActivityAt
Query takes 52 seconds to run, CPU usage hovers at 2-3%, Membery is 1.1GB, 900MB free but the Disk usage seems out of control. It's # 100MB/sec with 2/3 of that being writes to tempdb.mdf and the rest is reading from tempdb.mdf.
Now for the interesting part...
SELECT TOP(10) *
FROM Stories
INNER JOIN Categories ON Categories.ID = Stories.CategoryID
INNER JOIN Users ON Users.ID = Stories.UserID
SELECT TOP(10) *
FROM Stories
INNER JOIN Users ON Users.ID = Stories.UserID
ORDER BY Stories.LastActivityAt
SELECT TOP(10) *
FROM Stories
INNER JOIN Categories ON Categories.ID = Stories.CategoryID
ORDER BY Stories.LastActivityAt
All 3 of these queries are pretty much instant.
Exec plan for first query.
http://i43.tinypic.com/xp6gi1.png
Exec plans for other 3 queries (in order).
http://i43.tinypic.com/30124bp.png
http://i44.tinypic.com/13yjml1.png
http://i43.tinypic.com/33ue7fb.png
Any help would be much appreciated.
Exec plan after adding indexes (down to 17 seconds again).
http://i39.tinypic.com/2008ytx.png
I've gotten a lot of helpful feedback from everyone and I thank you, I tried a new angle at this. I query the stories I need, then in separate queries get the Categories and Users and with 3 queries it only took 250ms... I don't understand the issue but if it works and at 250ms no less for the time being I'll stick with that. Here's the code I used to test this.
DBDataContext db = new DBDataContext();
Console.ReadLine();
Stopwatch sw = Stopwatch.StartNew();
var stories = db.Stories.OrderBy(s => s.LastActivityAt).Take(10).ToList();
var storyIDs = stories.Select(c => c.ID);
var categories = db.Categories.Where(c => storyIDs.Contains(c.ID)).ToList();
var users = db.Users.Where(u => storyIDs.Contains(u.ID)).ToList();
sw.Stop();
Console.WriteLine(sw.ElapsedMilliseconds);
Try adding an index on Stories.LastActivityAt. I think the clustered index scan in the execution plan may be due to the sorting.
Edit:
Since my query returned in an instant with rows just a few bytes long, but has been running for 5 minutes already and is still going after I added a 2K varchar, I think Mitch has a point. It is the volume of that data that is shuffled around for nothing, but this can be fixed in the query.
Try putting the join, sort and top(10) in a view or in a nested query, and then join back against the story table to get the rest of the data just for the 10 rows that you need.
Like this:
select * from
(
SELECT TOP(10) id, categoryID, userID
FROM Stories
ORDER BY Stories.LastActivityAt
) s
INNER JOIN Stories ON Stories.ID = s.id
INNER JOIN Categories ON Categories.ID = s.CategoryID
INNER JOIN Users ON Users.ID = s.UserID
If you have an index on LastActivityAt, this should run very fast.
So if I read the first part correctly, it responds in 17 seconds with an index. Which is still a while to chug out 10 records. I'm thinking the time is in the order by clause. I would want an index on LastActivityAt, UserID, CategoryID. Just for fun, remove the order by and see if it returns the 10 records quickly. If it does, then you know it is not in the joins to the other tables. Also it would be helpful to replace the * with the columns needed as all 3 table columns are in the tempdb as you are sorting - as Neil mentioned.
Looking at the execution plans you'll notice the extra sort - I believe that is the order by which is going to take some time. I'm assuming you had an index with the 3 and it was 17 seconds... so you may want one index for the join criteria (userid, categoryID) and another for lastactivityat - see if that performs better. Also it would be good to run the query thru the index tuning wizard.
My first suggestion is to remove the *, and replace it with the minimum columns you need.
second, is there a trigger involved? Something that would update the LastActivityAt field?
Based on your problem query, try add a combination index on table Stories (CategoryID, UserID, LastActivityAt)
You are maxing out the Disks in your hardware setup.
Given your comments about your Data/Log/tempDB File placement, I think any amount of tuning is going to be a bandaid.
250,000 Rows is small. Imagine how bad your problems are going to be with 10 million rows.
I suggest you move tempDB onto its own physical drive (preferable a RAID 0).
Ok, so my test machine isn't fast. Actually it's really slow. It 1.6 ghz,n 1 gb of ram, No multiple disks, just a single (read slow) disk for sql server, os, and extras.
I created your tables with primary and foreign keys defined.
Inserted 2 categories, 500 random users, and 250000 random stories.
Running the first query above takes 16 seconds (no plan cache either).
If I index the LastActivityAt column I get results in under a second (no plan cache here either).
Here's the script I used to do all of this.
--Categories table --
Create table Categories (
[ID] [int] IDENTITY(1,1) primary key NOT NULL,
[ShortName] [nvarchar](8) NOT NULL,
[Name] [nvarchar](64) NOT NULL)
--Users table --
Create table Users(
[ID] [int] IDENTITY(1,1) primary key NOT NULL,
[Username] [nvarchar](32) NOT NULL,
[Password] [nvarchar](64) NOT NULL,
[Email] [nvarchar](320) NOT NULL,
[CreatedAt] [datetime] NOT NULL,
[LastActivityAt] [datetime] NOT NULL
)
go
-- Stories table --
Create table Stories(
[ID] [int] IDENTITY(1,1) primary key NOT NULL,
[UserID] [int] NOT NULL references Users ,
[CategoryID] [int] NOT NULL references Categories,
[VoteCount] [int] NOT NULL,
[CommentCount] [int] NOT NULL,
[Title] [nvarchar](96) NOT NULL,
[Description] [nvarchar](1024) NOT NULL,
[CreatedAt] [datetime] NOT NULL,
[UniqueName] [nvarchar](96) NOT NULL,
[Url] [nvarchar](512) NOT NULL,
[LastActivityAt] [datetime] NOT NULL)
Insert into Categories (ShortName, Name)
Values ('cat1', 'Test Category One')
Insert into Categories (ShortName, Name)
Values ('cat2', 'Test Category Two')
--Dummy Users
Insert into Users
Select top 500
UserName=left(SO.name+SC.name, 32)
, Password=left(reverse(SC.name+SO.name), 64)
, Email=Left(SO.name, 128)+'#'+left(SC.name, 123)+'.com'
, CreatedAt='1899-12-31'
, LastActivityAt=GETDATE()
from sysobjects SO
Inner Join syscolumns SC on SO.id=SC.id
go
--dummy stories!
-- A Count is given every 10000 record inserts (could be faster)
-- RBAR method!
set nocount on
Declare #count as bigint
Set #count = 0
begin transaction
while #count<=250000
begin
Insert into Stories
Select
USERID=floor(((500 + 1) - 1) * RAND() + 1)
, CategoryID=floor(((2 + 1) - 1) * RAND() + 1)
, votecount=floor(((10 + 1) - 1) * RAND() + 1)
, commentcount=floor(((8 + 1) - 1) * RAND() + 1)
, Title=Cast(NEWID() as VARCHAR(36))+Cast(NEWID() as VARCHAR(36))
, Description=Cast(NEWID() as VARCHAR(36))+Cast(NEWID() as VARCHAR(36))+Cast(NEWID() as VARCHAR(36))
, CreatedAt='1899-12-31'
, UniqueName=Cast(NEWID() as VARCHAR(36))+Cast(NEWID() as VARCHAR(36))
, Url=Cast(NEWID() as VARCHAR(36))+Cast(NEWID() as VARCHAR(36))
, LastActivityAt=Dateadd(day, -floor(((600 + 1) - 1) * RAND() + 1), GETDATE())
If #count % 10000=0
Begin
Print #count
Commit
begin transaction
End
Set #count=#count+1
end
set nocount off
go
--returns in 16 seconds
DBCC DROPCLEANBUFFERS
SELECT TOP(10) *
FROM Stories
INNER JOIN Categories ON Categories.ID = Stories.CategoryID
INNER JOIN Users ON Users.ID = Stories.UserID
ORDER BY Stories.LastActivityAt
go
--Now create an index
Create index IX_LastADate on Stories (LastActivityAt asc)
go
--With an index returns in less than a second
DBCC DROPCLEANBUFFERS
SELECT TOP(10) *
FROM Stories
INNER JOIN Categories ON Categories.ID = Stories.CategoryID
INNER JOIN Users ON Users.ID = Stories.UserID
ORDER BY Stories.LastActivityAt
go
The sort is definitely where your slow down is occuring.
Sorting mainly gets done in the tempdb and a large table will cause LOTS to be added.
Having an index on this column will definitely improve performance on an order by.
Also, defining your Primary and Foreign Keys helps SQL Server immensly
Your method that is listed in your code is elegant, and basically the same response that cdonner wrote except in c# and not sql. Tuning the db will probably give even better results!
--Kris
Have you cleared the SQL Server cache before running each of the query?
In SQL 2000, it's something like DBCC DROPCLEANBUFFERS. Google the command for more info.
Looking at the query, I would have an index for
Categories.ID
Stories.CategoryID
Users.ID
Stories.UserID
and possibly
Stories.LastActivityAt
But yeah, sounds like the result could be bogus 'cos of caching.
When you have worked with SQL Server for some time, you will discover that even the smallest changes to a query can cause wildly different response times. From what I have read in the initial question, and looking at the query plan, I suspect that the optimizer has decided that the best approach is to form a partial result and then sort that as a separate step. The partial result is a composite of the Users and Stories tables. This is formed in tempdb. So the excessive disk access is due to the forming and then sorting of this temporary table.
I concur that the solution should be to create a compound index on Stories.LastActivityAt, Stories.UserId, Stories.CategoryId. The order is VERY important, the field LastActivityAt must be first.