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The BulkInsert is using SqlBulkCopy internally.
Using SqlBulkCopy directly is about equally fast.
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Sander Rossel wrote: Yeah, we don't have the fastest database (50 DTUs), but it's plenty sufficient for everything else.
I ran some testing of our application with Azure SQL some time ago and found it to be very poor for an OLAP style IO bound workload, and probably checking a big index for uniqueness is similar. 50 DTU's on Basic or Standard tier is no more than 200 IOPS - DTU-based purchasing model - Azure SQL Database | Microsoft Learn[^]
For us it was better to use a SQL server on an Azure virtual machine, then we could do optimisations like striping the database across multiple smaller (unmanaged) hdd's using Windows Storage Spaces. Nowadays there are probably better managed disk options with SSD's etc to get you a decent IO performance level. Configure storage for SQL Server VMs - SQL Server on Azure VMs | Microsoft Learn[^]
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50 DTU's on Standard tier is more than enough for everything else we have.
It's not an OLAP application, it's more administrative.
It has some master data, you can import and edit orders (it's this importing that's causing me headaches) and some one-off reports and other functionality.
The import lines (about 2500 a file) are grouped, some prices are calculated and those prices are added per group in code, which is super fast.
Other than that, the lines are printed on a report.
All in all it's a pretty small application that ties the production and the financial software together.
The database currently stores 3.38 GB of data of which this import table is about 2/3rds (and that's all we do with those lines) 
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Inspired by this question, I went to take a more detailed look at where the time to load a 1.2 million line, 0.5 gigabyte, CSV file goes in a particular application that I'm working on. That process takes about 5 seconds too, by some coincidence.
There are three steps: reading the file (all at once), converting the encoding, and parsing the CSV into objects.
- reading the file (File.ReadAllBytes) takes about 150 milliseconds. (so we're reading the file at 3.3GB/s, which is reasonable, near the maximum sequential read speed of the SSD)
- converting the encoding (aka Encoding.GetString) takes about 900 milliseconds. (at 5GHz, this comes down to 9 cycles per byte, should it take 9 cycles per byte to do encoding conversion? sounds slow to me)
- parsing / creating objects takes the rest of the time. (this code is crap and takes at least 10 times as long as it should)
There's no database involved. Loading 2k rows in the same time basically comes down to saying that using a database makes loading the data over 500 times as slow - compared to code that is slow already. Is that reasonable? Databases are infamous for being pure molasses, but that sounds extreme.
On the other hand, it's only 5 seconds, not a big deal.
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harold aptroot wrote: load a 1.2 million line, 0.5 gigabyte, CSV file
harold aptroot wrote: Databases are infamous for being pure molasses, but that sounds extreme.
Well, at 2500 lines and 40 values per line, it comes down to storing 100,000 values in the correct place.
Storing the CSV directly takes a few 100 milliseconds.
The database actions are the only slow lines of code here.
Reading the CSV and putting it to objects is milliseconds (and that's using a general CSV parser that uses reflection).
harold aptroot wrote: using a database makes loading the data over 500 times as slow
I guess that sounds about right.
Depending on how fast your DB server is, of course 
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What David said. I have similar requirements for updating a huge table (or set of tables) with a relatively small CSV dataset. I put the CSV into a separate table, do the data validation (like duplicate checks) with SQL and indexed columns on the criteria for duplicates, and perform the resulting updates/inserts in C# and email the customer with the change log. Very very fast.
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How do you handle validation towards your users?
A user imports a file and wants to know why it failed (e.g. some value is invalid or the file was already (partly) imported).
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Well, since these updaters run as an automated process, the customer gets an email.
Internally, all errors, warnings, and performed actions are logged, so if it were a manually initiated process, we could provide a full report to the user on the website as well.
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Your answer is right here on CP: A Fast CSV Reader[^]
That article is not written by me, but I've used this component since sometime way back in 2008 or so.
It has been updated along the way.
That is a fantastic and amazingly fast (see perf stats in the article).
It's super easy to use and will solve your problems fast. I remember getting it to work in about 15 minutes. Import the component into your project & the API calls are intuitive.
Seriously try it and I'm sure it'll make you happy. 
Let me know what you think.
Here's a snippet of the performance from the article:
From article: To give more down-to-earth numbers, with a 45 MB CSV file containing 145 fields and 50,000 records, the reader was processing about 30 MB/sec. So all in all, it took 1.5 seconds! The machine specs were P4 3.0 GHz, 1024 MB.
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I'm pretty sure it's a great tool, but it's not the CSV I'm having trouble with.
I get all my values in objects in mere milliseconds with my own generic CSV reader.
Besides, this project uses .NET Framework 2.0, which is like 15 versions, two complete overhauls and twenty years behind my version of .NET 
My problem is getting all those values in a database in an acceptable time frame (and 30+ seconds is not acceptable  ).
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Have a session when they are doing the process and see why the time out occurs. What changed ? Was it working ok before? Why is it taking 17 tries now? maybe u can do a sql monitor and see the iops/sessions etc....
Caveat Emptor.
"Progress doesn't come from early risers – progress is made by lazy men looking for easier ways to do things." Lazarus Long
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This is a nightmare scenario: Quote: Of course it wasn't always like that, but the import table in Azure SQL has over 2 million records now and I need to validate for double records,
I've done this before and it's slow no matter what I do and our db is very optimized. What I wound up doing was pulling and storing in an array, all of the unique fields only. It's one pull (huge, but with only one or two values per record, doable). Then in the double-record validation, I just check each new record against that one array, in advance of any db insert/update work, sorting the new records into two arrays, one for insert, one for update. That way the inserts are quicker. If you are trashing the duplicates, you can skip the update part.
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So, you've isolated it to the DB load. In ghost jobs of long past, I know inserting records into tables with indexes could bite you, since the db has to do it's thing. The application was a "real-time" database that gated prepaid phone calls. Although we didn't do batch operations, we might have anywhere from 512-4096 call handler threads all hitting the database relatively at the same time (don't want to get pedantic) . However, most of these transactions were either reads or updates. Creation occurred only for new customers.
So, lots of index use, but no new index entries.
Please add a reminder to your task list to update the thread with what the actual problem is and how you solved it 
Oh, one other suggestion - it sounds like to me you could collect these files and set up an offline test system to play - remove the web complexity. I know you've done this a long time, so you probably already are doing this.
Charlie Gilley
“They who can give up essential liberty to obtain a little temporary safety deserve neither liberty nor safety.” BF, 1759
Has never been more appropriate.
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Interesting. We have issues with CSV upload but it is in the upload failing itself, which is being used due to the lack of an API from an outside vendor. It's AWS to local. That's a topic in itself with a long, dumb winding road with a large vendor. Haven't had issues with Azure connects, though.
My first question is process: It sounds like you have control over both ends of this? If so, why a CSV rather than a direct upsert? Five seconds feels like an eternity (it does really feel that way today!) but CSVs to DB with validation takes a while. Our environment is different, using Postgres on a nix with a triggered background cron and our line count is only in the hundreds per upload, so numbers don't compare. What's an import "table" and why does it exist, or is that just wording for a real destination table?
This caught my eye: Quote: Not so much an issue in my development environment, but apparently a bottleneck in production.
You mean the time just isn't an issue or does it run faster? Not sure why 5 seconds would create a "bottleneck" in production with five uploads a day? The timeout is generated on your end, right? Bump it up or calc it on estimated process time. If it ultimately works, let it run, no? Yeah, it's annoying looking at a spinner for a bit, but even if it's 30 seconds five times a day, no one is that busy.
Sorry, haven't worked with SQL Server in years but we do find the more you can put into a longer SQL query, the better the performance vs outside processing. Again, with SQL Server caveat above, I'd also index the heck out of it with 2 mil records.
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I would implement a tmp table with a twist: diff
I would create a table, lets call it "ImportData" with 3 columns:
ID: i assume there is a field on the CSV that is unique
Data: the complete CVS line
Status: a importa status depending on the operation
I like char instead of int with "magical numbers" for these kinds os status
A single char will not hurt performance
Then:
1: Set the status to '?' before importing
2: Import to this table row by row
3: Have a SQL trigger that compares the value of column 'Data'
If it changed, set Status to 'U' (Updated) if did not change set status to 'N' (NOP, No operation)
4: At the end, set all rows with '?' to 'D' (Deleted), these are the rows not present on the CSV
5: Process all rows with status different from 'N' (the changed ones)
If 'U', insert or update on main table, if 'D' delete from main table
This will give you a differential update with only the changed rows being updated in your Main data table.
Will also give you a 'snapshot' of all data received in the last CSV file. Very useful for debug
I have several imports done this way, usually imports are very fast since (in my case) most CSV rows do not change.
If the import data is XML, o JSON you can do the same by saving the XML node or JSON node on the 'Data' column.
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This is a huge area with all sorts of possible ways of solving it.
I currently work on a rather huge codebase, part of which performs automated imports and exports in a batching system.
That would probably be overkill for what you are wanting.
There isn't a one solution for everything in this sort of case - however a staging table is always a good place to start - import the data into a staging table then you can decide on a strategy from there.
The strategy might involve selectively picking records that are then fed into your main table's or even allowing the user to choose the data.
The advantage of a staging table is that you can isolate any issues within the staging table as well as be able to query it and analyse data from there without affecting your main tables.
As for timings - for something like an import there should really be no guarantees of how many seconds it takes because there are so many variables out of your control(connection speed, number of records, record sizes etc.)
What you can do is provide some sort of progress bar to let the user know what stage things are at(I actually deliberately slow the progress bar in some applications so that users get a sense of something happening rather than something flashing on and off the screen).
“That which can be asserted without evidence, can be dismissed without evidence.”
― Christopher Hitchens
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This falls under the 'checking to make sure I'm not leaving my drink on the roof of my car before driving off' category of suggestions.
You may want to make sure the database connection is not closing and reconnecting between each record. This is something I've run into back in my old Access days. 
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I had a similar issue where I needed to import 10's of thousands of records every day, most of them duplicates. We only inserted if the record didn't already exist. It was a serious performance bottleneck.
I generated hashes for all of the existing rows and added an index for the new hash column. Now I calculate the hash of the values in each new row to be inserted and compare the hash to existing rows. I skip the insert if an existing identical row already exists. Using the index solved the insert performance problem for me.
I don't recommend using MD5 or similar simple hashes because it's too easy to create duplicate hashes.
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- Figure out the bottle neck.
Since it does not happen in Dev, but does in production, I suspect it is the duplicate checking. If you size dev the same as production will it reproduce in dev?
Also, do you have more fault tolerance/active nodes in production? If you are doing a few thousand small transactions in a high availability setup, it will take longer.
Do you perform all of your duplicate checks in a single query? Or one import row at a time?
If you setup good, unique indexes, then you can skip the duplicate checking and let the DB do it for you. This would point you toward a commit per import row.
We had an import feature that ended up with an unnecessary exponential complexity. It was fine during testing but started getting really slow at only 150 rows.
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