Below Image is my database struture image.
I have 100+ sensors which are sending constant data to respective machines.
I have 5-7 different machines which is having different SQL express database installed in it.
All the machines will send its respective data to one SERVER.
Every second each mahcine will send 10 rows as a bulk data to server stored procedure
PROBLEM : managing large data coming from every machines to single server to avoiding deadlock / delay in performance.
Background Logic
Bulk data from machines are stored into temporary table
& than using that temp table i am looping through each record for processing.
Finally in sp_processed_filtered_data lots of insert & updates are present. & there are nested sp's for processing that filtered data.
Current Logic :
Step : 1
Every Machine will send data to SP_Manage stored procedure which consist of bulk data in XML format, which we are converting in SQL table format.
This data is row data. so we filter this data.
Let's say after filtering 3 rows are remained.
so I want to process that each row now.
Step : 2
As I have to process each rows I have to loop through each row to send data to SP_Process_Filetered_Data.
Now this SP is containing complex logic.
I am looping though each records & every machines will send data parallelly.
So I am afraid will it be causing data loss or dead lock.
Related
We are working building a new data pipeline for our project and we have to move incremental updates that happen throughout the day on our SQL servers into Azure synapse for some number crunching.
We have to get updates which occur across 60+ tables ( 1-2 million updates a day ) into synapse to crunch some aggregates and statistics as they happen throughout the day.
One of the requirements is being near real time and doing a bulk import into synapse is not ideal because it takes more than 10 mins to do full compute on all data.
I have been reading about CDC feed into synapse https://learn.microsoft.com/en-us/azure/data-factory/tutorial-incremental-copy-change-data-capture-feature-portal and it is one possible solution.
Wondering if there are other alternatives to this or suggestions for achieving the end goal of data crunching near real time for DB updates.
Change Data Capture (CDC) is the suited way to capture the changes and add to the destination location (storage/database).
Apart from that, you can also use watermark column to capture the changes in multiple tables in SQL Server.
Select one column for each table in the source data store, which you
can identify the new or updated records for every run. Normally, the
data in this selected column (for example, last_modify_time or ID)
keeps increasing when rows are created or updated. The maximum value
in this column is used as a watermark.
Here is the high-level solution diagram for this approach:
Step-by-Step approach is given in this official document Incrementally load data from multiple tables in SQL Server to Azure SQL Database using PowerShell.
I have 2 tables. The source table being from a linked server and destination table being from the other server.
I want my data load to happen in the following manner:
Everyday at night I have scheduled a job to do a full dump i.e. truncate the table and load all the data from the source to the destination.
Every 15 minutes to do incremental load as data gets ingested into the source on second basis. I need to replicate the same on the destination too.
For incremental load as of now I have created scripts which are stored in a stored procedure but for future purposes we would like to implement SSIS for this case.
The scripts run in the below manner:
I have an Inserted_Date column, on the basis of this column I take the max of that column and delete all the rows that are greater than or equal to the Max(Inserted_Date) and insert all the similar values from the source to the destination. This job runs evert 15 minutes.
How to implement similar scenario in SSIS?
I have worked on SSIS using the lookup and conditional split using ID columns, but these tables I am working with have a lot of rows so lookup takes up a lot of the time and this is not the right solution to be implemented for my scenario.
Is there any way I can get Max(Inserted_Date) logic into SSIS solution too. My end goal is to remove the approach using scripts and replicate the same approach using SSIS.
Here is the general Control Flow:
There's plenty to go on here, but you may need to learn how to set variables from an Execute SQL and so on.
Requirement :
Transfer millions of records from source (SQL Server) to destination (SQL Server).
Structure of source tables is different from destination tables.
Refresh data once per week in destination server.
Minimum amount of time for the processing.
I am looking for optimized approach using SSIS.
Was thinking these options :
Create Sql dump from source server and import that dump in destination server.
Directly copy the tables from source server to destination server.
Lots of issues to consider here. Such as are the servers in the same domain, on same network, etc.
Most of the time you will not want to move the data as a single large chunk of millions of records but in smaller amounts. An SSIS package handles that logic for you, but you can always recreate it as well but iterating the changes easier. Sometimes this is a reason to push changes more often rather than wait an entire week as smaller syncs are easier to manage with less downtime.
Another consideration is to be sure you understand your delta's and to ensure that you have ALL of the changes. For this reason I would generally suggest using a staging table at the destination server. By moving changes to staging and then loading to the final table you can more easily ensure that changes are applied correctly. Think of the scenario of a an increment being out of order (identity insert), datetime ordered incorrectly or 1 chunk failing. When using a staging table you don't have to rely solely on the id/date and can actually do joins on primary keys to look for changes.
Linked Servers proposed by Alex K. can be a great fit, but you will need to pay close attention to a couple of things. Always do it from Destination server so that it is a PULL not a push. Linked servers are fast at querying the data but horrible at updating/inserting in bulk. 1 XML column cannot be in the table at all. You may need to set some specific properties for distributed transactions.
I have done this task both ways and I would say that SSIS does give a bit of advantage over Linked Server just because of its robust error handling, threading logic, and ability to use different adapters (OLEDB, ODBC, etc. they have different performance do a search and you will find some results). But the key to your #4 is to do it in smaller chunks and from a staging table and if you can do it more often it is less likely to have an impact. E.g. daily means it would already be ~1/7th of the size as weekly assuming even daily distribution of changes.
Take 10,000,000 records changed a week.
Once weekly = 10mill
once daily = 1.4 mill
Once hourly = 59K records
Once Every 5 minutes = less than 5K records
And if it has to be once a week. just think about still doing it in small chunks so that each insert will have more minimal affect on your transaction logs, actual lock time on production table etc. Be sure that you never allow loading of a partially staged/transferred data otherwise identifying delta's could get messed up and you could end up missing changes/etc.
One other thought if this is a scenario like a reporting instance and you have enough server resources. You could bring over your entire table from production into a staging or update a copy of the table at destination and then simply do a drop of current table and rename the staging table. This is an extreme scenario and not one I generally like but it is possible and actual impact to the user would be very nominal.
I think SSIS is good at transfer data, my approach here:
1. Create a package with one Data Flow Task to transfer data. If the structure of two tables is different then it's okay, just map them.
2. Create a SQL Server Agent job to run your package every weekend
Also, feature Track Data Changes (SQL Server) is also good to take a look. You can config when you want to sync data and it's good at performance too
With SQL Server versions >2005, it has been my experience that a dump to a file with an export is equal to or slower than transferring data directly from table to table with SSIS.
That said, and in addition to the excellent points #Matt makes, this the usual pattern I follow for this sort of transfer.
Create a set of tables in your destination database that have the same table schemas as the tables in your source system.
I typically put these into their own database schema so their purpose is clear.
I also typically use the SSIS OLE DB Destination package's "New" button to create the tables.
Mind the square brackets on [Schema].[TableName] when editing the CREATE TABLE statement it provides.
Use SSIS Data Flow tasks to pull the data from the source to the replica tables in the destination.
This can be one package or many, depending on how many tables you're pulling over.
Create stored procedures in your destination database to transform the data into the shape it needs to be in the final tables.
Using SSIS data transformations is, almost without exception, less efficient than using server side SQL processing.
Use SSIS Execute SQL tasks to call the stored procedures.
Use parallel processing via Sequence Containers where possible to save time.
This can be one package or many, depending on how many tables you're transforming.
(Optional) If the transformations are complex, requiring intermediate data sets, you may want to create a separate Staging database schema for this step.
You will have to decide whether you want to use the stored procedures to land the data in your ultimate destination tables, or if you want to have the procedures write to intermediate tables, and then move the transformed data directly into the final tables. Using intermediate tables minimizes down time on the final tables, but if your transformations are simple or very fast, this may not be an issue for you.
If you use intermediate tables, you will need a package or packages to manage the final data load into the destination tables.
Depending on the number of packages all of this takes, you may want to create a Master SSIS package that will call the extraction package(s), then the transformation package(s), and then, if you use intermediate processing tables, the final load package(s).
I'm looking for a way to speed up the following process: I have a SSIS package that loads data from Excel files on a weekly basis to SQL Server. There are 3 fields: Brand, Date, Value.
In the dataflow, I check for existing combinations of Brand+Date, and new combinations go to the table directly, the existing ones go to a RecordSet destination for updates:
The next step is to update the Value of the existing combinations:
As you can see, there are thousands of records to update, and it takes too long. The number of records tend to grow week by week. Please suggest.
The fastest way will be do this inside a Stored procedure using ELT (Extract Load Transform) approach.
Push all data from excel as is into a table(called load to a staging table in theory). Since you do not seem to be concerned with data validation steps, this table can be a replica of final destination table columns.
Next step is to call a stored procedure using Execute SQL task. Inside this procedure you can put all your business logic. Since this steps with native data manipulation on SQL server entities, it is the fastest alternative.
As a last part, please delete all entries from the staging table.
You can use indexes on staging table to make the SP part even faster.
I am inserting data into a database using millions of insert statements stored in a file. Is it better to insert this row by row or in bulk ? I am not sure what the implications can be.
Any suggestions on the approach ? Right now, I am executing 50K of these statements at a time.
Generally speaking, you're much better off inserting in bulk, provided you know that the inserts won't fail for some reason (i.e. invalid data, etc). If you're going row by row, what you're doing, is opening the data connection, adding the row, closing the data connection. Rinse wash, repeat in your case tens of thousands of times (or more?). It's a huge performance hit as opposed to opening the connection once, dumping all the data at one shot, then closing the connection once. If your data ISN'T a clean set of data, you might be better off going row by row, as the bulk insert won't fail if you have data to be cleaned up.
If you are using SSIS, I would suggest a data flow task as another possible avenue. This will allow you to move data from a flat text file, SQL table or other source and map it into your new table. Performance, I have found, is always pretty good and I use it regularly.
If your table is not created before the insert, what I do is drag an Execute SQL Task function into my process with the table creation query (CREATE TABLE....etc.) and update the properties on the data flow function to delay validation.
As long as my data structure is consistent, this works. Here are a couple screenshots.
You should definitely use the BULK INSERT instead of inserting row by row. The BULK INSERT is the in-process method designed for bringing data from a text file into SQL Server, ant it is the fasted among other approaches described in the The Data Loading Performance Guide online article
The other alternative is to use a batch process that uses set-based processing over a smaller set of records (say 5000 at a time) . This can keep the server from getting totally locked up and is faster than one record at a time.