We have an SSIS package that is apparently termed as 'slow' by the development team. Since they do not have a person with SSIS ETL, as a DBA I tried digging into it. Below is the information I found:
SQL Server was 2014 version upgraded -inplace to 2017 so it has SSIS of both versions.
They load a SQL Server table of size 200 GB into SSIS and then zip the data into flatfile using command line zip functionality.
The data flow task simple hits a select * from view - the view is nothing but containing the table with no other fancy joins.
While troubleshooting I found that on SQL Server, there is hardly any load coming, possibly because the select command is running in single thread and not utilizing SQL server cores.
When I run the same select * command (only for 5 seconds, since it is 200 GB table), even my command is single threaded.
The package has a configuration file that the SQL job shows (this is how the package runs) with some connection settings.
Opening the package in BIDS show defaultBufferMaxRows as 10000 only (possibly default value) (since configuration file or any variables does not has a customer value, I guess this is what the package is using too).
Both SQL and SSIS are on same server. SQL has been allocated max memory leaving around 100 GB for SSIS and OS.
Kindly share any ideas on how can I force the SQL Server to run this select command using multiple threads so that entire table gets inside SSIS buffer pool faster.
Edit: I am aware that bcp can read data faster than any process and save it to flatfile but at this point changes to the SSIS package has to be kept minimum and exploring options that can be incorporated within SSIS package.
Edit2: Parallelism works perfectly for my SQL Server as I verified for a lot of other queries.The table in question is 200 GB. It is something with SSIS only which is not hammering my DB as hard as it should.
Edit3: I have made some progress, adjusted the buffer value to 100 MB and max rows to 100000 and now the package seem to be doing better. when I run this package on the server directly using dtexec utility, it generates good load of 40- 50 MB per second but through SQL job it never generates lod more than 10 MB. so I am trying to figure out this behavior.
Edit4: I found that when I run the package directly from logging to the server and invoking dtexec utility, it runs good because it generates good load on the DB causing data I\O to remain steady between 30-50 MB\sec.
The same thing from SQL job never exceeds the I\O more than 10 MB\sec.
I even tried to run the package using agent and opting for cmdline operation but no changes. Agent literally sucks here, any pointers on what could be wrong here?
Final Try:
I am stumped at the observation I have finally:
1)Same package runs 3x faster when run from command prompt from windows node by invoking dtexc utility
2) Exact same package runs 3 times slower than above when involked by SQL agent which has sysadmin permissions on windows as well as SQL Server
In both cases, I tried to see the version of DTEXEC they invoke, and they both invoke the same version. So why one would be so slow is out of my understanding.
I don't think that there is a general solution to this issue since it is a particular case that you didn't provide much information. Since there are two components in your data flow task (OLE DB Source and Flat File Destination), I will try to give some suggestions related to each component.
Before giving suggestions for each component, it is good to mention the following:
If no transformations are applied within the data flow task, It is not recommended to use this task. It is preferable to use bcp utility
Check the TempDb and the database log size.
If a clustered index exists, try to rebuild it. If not, try to create a clustered index.
To check the component that is slowing the package execution, open the package in Visual Studio and try to remove the flat file destination and replace it with a dummy Script Component (write any useless code, for example: string s = "";). And then run the package; if it is fast enough, then the problem is caused by the Flat File Destination, else you need to troubleshoot the OLE DB Source.
Try executing the query in the SQL Server management studio and shows the execution plan.
Check the package TargetServerVersion property within the package configuration and make sure it is correct.
OLE DB Source
As you mentioned, you are using a Select * from view query where data is stored in a table that contains a considerable amount of data. The SQL Server query optimizer may find that reading data using Table Scan is more efficient than reading from indexes, especially if your table does not have a clustered index (row store or column store).
There are many things you may try to improve data load:
Try replacing the Select * from view with the original query used to create the view.
Try changing the data provider used in the OLE DB Connection Manager: SQL Server Native Client, Microsoft OLE DB provider for SQL Server (not the old one).
Try increasing the DefaultBufferMaxRows and DefaultBufferSize properties. more info
Try replacing using SQL Command with specific column names instead of selecting the view name (Table of View data access mode). more info
Try to load data in chunks
Flat File Destination
Check that the flat file directory is not located on the same drive where SQL Server instance is installed
Check that the flat file is not located on a busy drive
Try to export data into multiple flat files instead of one huge file (split data into smaller files) , since when the exported data size increase in a single file, writing to this file become slower, then the package will become slower. (Check the 5th suggestion above)
Any indexes on the table could slow loading. If there are any indexes, try dropping them before the load and then recreating them after. This would also update the index statistics, which would be skewed by the bulk insert.
Are you seeing SQL server utilizing other cores too for other queries? If not, maybe someone played with the following settings:
Check these under server configuration setting:
Maximum Degree of Parallelism
Cost Threshold for Parallelism (server configuration setting).
Does processors affinitized to a CPU.
Also, MaxDOP query hint can cause this too but you said there is no fancy stuff in the view.
Also, it seems you have enough memory on error, why not increase defaultBufferMaxRows to an extremely large number so that SQL server doesn't get slowed down waiting for the buffer to get empty. Remember, they are using the same disk and they will have to wait for each other to use the disk, which will cause extra wait times for the both. It's better SQL server uses it, put into the buffer, and then SSIS starts processing and writing it into disk.
DefaultBufferSize : default is 10MB, max possible 2^31-1 bytes
DefaultBufferMaxRows : default is 10000
you can set AutoAdjustBufferSize so that DefaultBufferSize is automatically calculated based on DefaultBufferMaxRows
See other performance troubleshooting ideas here
https://learn.microsoft.com/en-us/sql/integration-services/data-flow/data-flow-performance-features?view=sql-server-ver15
Edit 1: Some other properties you can check out. These are explained in the above link as well
MaxConcurrentExecutables (package property): This defines how many threads a package can use.
EngineThreads (Data Flow property): how many threads the data flow engine can use
Also try running dtsexec under the same proxy user used by SQL agent to see if you get different result with this account versus your account. You can use runas /user:... cmd to open a command window under that user and then execute dtexec.
Try changing the proxy user used in SQL Agent to a new one and see if it will help. Or try giving elevated permissions in the directories it needs access to.
Try keeping the package in file-system and execute through dtexec from the SQL Agent directly instead of using catalog.start_execution.
Not your case but for other readers: if you have "Execute Package Task", make sure the child packages to be executed are set to run in-process via ExecuteOutOfProcess property. This just reduces overhead of using more processes.
Not your case but for other readers: if you're testing in BIDS, it will run in debug mode by default and thus run slow. Use CTRL-F5 (start without debugging). The best is to use dtexec directly to test the performance
A data flow task may not be the best choice to move this data. SSIS Data Flow tasks are an ETL tool where you can do transformations, look ups, redirect invalid rows, add derived columns and a lot more. If the data flow task is simple and only moves data with no manipulation or redirection of rows then ditch the Data Flow task and use a simple Execute SQL Task and OPENROWSET to import the flat file that was generated from command line and zipped up. Assuming the flat file is a .csv file here are some working examples to query a .csv and insert the data to a table.
You need [Ad Hoc Distributed Queries] run_value set to 1
into dbo.Destination
SELECT *
from openrowset('MSDASQL', 'Driver={Microsoft Text Driver (*.txt; *.csv)};
DefaultDir=D:\YourCsv.csv;Extensions=csv;','select * from YourCsv.csv') File;
Here is some additional examples https://sqlpowershell.blog/2015/02/09/t-sql-read-csv-files-using-openrowset/
There are suggestions in this MSDN article: MSDN DataFlow performance features
Key ones appear to be:
Check the EngineThreads property of the DataFlow task, which tells SSIS how may source and worker threads it should use
If using OLE DB Source to select data from a view uses "SQL Command" and write a SELECT * From View rather than Table or View
Let us know how you get on
You may be facing I/O bottleneck while writing the 200GB to the flat file. I don't see any problem with SQL Query.
If possible create multiple files and split the data (either by modifying SSIS or changing the select query)
Loading data from my OLTP database (it's part of ETL) via OPENQUERY or SSIS Data Flow to another SQL Server database (Warehouse which run this SSIS package / OPENQUERY statement), kills it. As I checked in Performance Monitor I use resources from source database, not from destiny. Is possible to reverse this resource utilization (using SQL Server 2016 or SSIS)?
The problem here is in your destination write operation. If you are using OLE DB Destination with fast load access mode try setting the rows per batch value to a non-zero value and reduce the maximum insert commit size to a value that will be easy on your memory and CPU. SSIS will not have to wait for the default of 2147483647 before writing to the destination table which can have a large impact on your log file slowing your process down. Please refer to this Article for more info on setting this values. All the best
How does your export query looks like? Is it just a simple data dump or do you have some complex logic in (e.g. doing some denormalization/aggregation with the export)?
If it's just a simple export, check on which server your SSIS package runs and what resources it uses. In any case, you need to read the data from your source system, so expect some read disc operations.
In general it is better to get the data from an OLTP as quickly as possible and then apply other operations in further steps of your ETL process on your ETL/Data warehouse server. In order to reduce an impact on your transactional system.
Hope it helps.
I am using SQLLite with MS SQL Reporting Services via SQL Lite ODBC. The reports are quite complex and causes too many disk reads, sometimes taking over 5 minutes to generate a report. In order the improve performance, tried to do the following:
Restoring the database file (or its backup) into a in memory database and then ran the query
> .restore c:/mypath/a.db;
> select * from mytable;
Works great command line, but ODBC tells me that I cannot execute these two commands at once.
Is there a way with SQLite ODBC that I can pin a SQLite DB In memory to overcome disk read issues?
.restore is not an SQL statement; it's implemented by the sqlite3 command-line shell.
There is no mechanism to pin an existing file-based database in memory. However, if it fits into memory, the OS will keep it in the file cache anyway.
My current environment is: 3 servers, one for source database, one for destination database and one for IS packages. Now I need to adjust the configurations, like CPU, Memory for each server .
I do believe that running IS packages will consume a lot of resources because of large data volume. However, I do not know which server needs to be configured with more power for IS packages. That is, which server's resource will be mostly used when IS is running?
Also, I need to setup SQL agent for daily ETL processing, then which DB server I should use, the source or destination one?
I'm new to IS deployment,thanks for any advice!
The data will be read from the source server and written to the destination server, so here you need nice fast IO subsystem. Ideally RAID 10. Also, providing your data is split across multiple discs on the source server, more cores will achieve more parallelism. This is not so important on the destination as inserts are normally single threaded.
The server running SSIS needs lots of memory as the data flow buffers will be on this server (providing you run Server Agent here) and you need a fast network connection between all three.
Server Agent should be on the ETL server, otherwise SSIS will consume resources on the box that Server Agent is on, and could therefore fight for threads with SQL Server whilst reading or writing.
I'm maintaining a legacy server app that generate DMO files from SQL Server views.
Sometimes the server crashes because SQL Server consumes all cpu resources.
Using the SQL Server monitor I see that the problem is in SQLDMO connections that are consuming all cpu time and blocking the server.
I don't understand the reason of that because the dmo connection is with TRANSACTION LEVEL READ UNCOMMITTED and these SQLs never finish, during weeks. The only solution is to shutdown the server.
I would suggest looking into the code why these connections are not closed. I'm guessing there's no proper closing at the end or something along those lines.
If that is not an option, you could consider running a scheduled job that kills off these specific jobs every so often if they ran for longer than say, 24 hours.