What's the most efficient method to load large volumes of data from CSV (3 million + rows) to a database.
The data needs to be formatted(e.g. name column needs to be split into first name and last name, etc.)
I need to do this in a efficiently as possible i.e. time constraints
I am siding with the option of reading, transforming and loading the data using a C# application row-by-row? Is this ideal, if not, what are my options? Should I use multithreading?
You will be I/O bound, so multithreading will not necessarily make it run any faster.
Last time I did this, it was about a dozen lines of C#. In one thread it ran the hard disk as fast as it could read data from the platters. I read one line at a time from the source file.
If you're not keen on writing it yourself, you could try the FileHelpers libraries. You might also want to have a look at Sébastien Lorion's work. His CSV reader is written specifically to deal with performance issues.
You could use the csvreader to quickly read the CSV.
Assuming you're using SQL Server, you use csvreader's CachedCsvReader to read the data into a DataTable which you can use with SqlBulkCopy to load into SQL Server.
I would agree with your solution. Reading the file one line at a time should avoid the overhead of reading the whole file into memory at once, which should make the application run quickly and efficiently, primarily taking time to read from the file (which is relatively quick) and parse the lines. The one note of caution I have for you is to watch out if you have embedded newlines in your CSV. I don't know if the specific CSV format you're using might actually output newlines between quotes in the data, but that could confuse this algorithm, of course.
Also, I would suggest batching the insert statements (include many insert statements in one string) before sending them to the database if this doesn't present problems in retrieving generated key values that you need to use for subsequent foreign keys (hopefully you don't need to retrieve any generated key values). Keep in mind that SQL Server (if that's what you're using) can only handle 2200 parameters per batch, so limit your batch size to account for that. And I would recommend using parameterized TSQL statements to perform the inserts. I suspect more time will be spent inserting records than reading them from the file.
You don't state which database you're using, but given the language you mention is C# I'm going to assume SQL Server.
If the data can't be imported using BCP (which it sounds like it can't if it needs significant processing) then SSIS is likely to be the next fastest option. It's not the nicest development platform in the world, but it is extremely fast. Certainly faster than any application you could write yourself in any reasonable timeframe.
BCP is pretty quick so I'd use that for loading the data. For string manipulation I'd go with a CLR function on SQL once the data is there. Multi-threading won't help in this scenario except to add complexity and hurt performance.
read the contents of the CSV file line by line into a in memory DataTable. You can manipulate the data (ie: split the first name and last name) etc as the DataTable is being populated.
Once the CSV data has been loaded in memory then use SqlBulkCopy to send the data to the database.
See http://msdn.microsoft.com/en-us/library/system.data.sqlclient.sqlbulkcopy.writetoserver.aspx for the documentation.
If you really want to do it in C#, create & populate a DataTable, truncate the target db table, then use System.Data.SqlClient.SqlBulkCopy.WriteToServer(DataTable dt).
Related
I have to parse a big XML file and import (insert/update) its data into various tables with foreign key constraints.
So my first thought was: I create a list of SQL insert/update statements and execute them all at once by using SqlCommand.ExecuteNonQuery().
Another method I found was shown by AMissico: Method
where I would execute the sql commands one by one. No one complained, so I think its also a viable practice.
Then I found out about SqlBulkCopy, but it seems that I would have to create a DataTable with the data I want to upload. So, SqlBulkCopy for every table. For this I could create a DataSet.
I think every option supports SqlTransaction. It's approximately 100 - 20000 records per table.
Which option would you prefer and why?
You say that the XML is already in the database. First, decide whether you want to process it in C# or in T-SQL.
C#: You'll have to send all data back and forth once, but C# is a far better language for complex logic. Depending on what you do it can be orders of magnitude faster.
T-SQL: No need to copy data to the client but you have to live with the capabilities and perf profile of T-SQL.
Depending on your case one might be far faster than the other (not clear which one).
If you want to compute in C#, use a single streaming SELECT to read the data and a single SqlBulkCopy to write it. If your writes are not insert-only, write to a temp table and execute as few DML statements as possible to update the target table(s) (maybe a single MERGE).
If you want to stay in T-SQL minimize the number of statements executed. Use set-based logic.
All of this is simplified/shortened. I left out many considerations because they would be too long for a Stack Overflow answer. Be aware that the best strategy depends on many factors. You can ask follow-up questions in the comments.
Don't do it from C# unless you have to, it's a huge overhead and SQL can do it so much faster and better by itself
Insert to table from XML file using INSERT INTO SELECT
I have a very large number of rows (10 million) which I need to select out of a SQL Server table. I will go through each record and parse out each one (they are xml), and then write each one back to a database via a stored procedure.
The question I have is, what's the most efficient way to do this?
The way I am doing it currently is I open 2 SqlConnection's (one for read one for write). The read one uses the SqlDataReader of which it basically does a select * from table and I loop through the dataset. After I parse each record I do an ExecuteNonQuery (using parameters) on the second connection.
Is there any suggestions to make this more efficient, or is this just the way to do it?
Thanks
It seems that you are writing rows one-by-one. That is the slowest possible model. Write bigger batches.
There is no need for two connections when you use MARS. Unfortunately, MARS forces a 14 byte row versioning tag in each written row. Might be totally acceptable, or not.
I had very slimier situation and here what I did:
I made two copies of same database.
One is optimized for reading and another is optimized for writing.
In config, i kept two connection string ConnectionRead and ConnectionWrite.
Now in DataLayer when I have read statement(select..) I switch my connection to ConnectionRead connection string and when writing using other one.
Now since I have to keep both the databases in sync, I am using SQL replication for this job.
I can understand implementation depends on many aspect but approach may help you.
I agree with Tim Schmelter's post - I did something very similar... I actually used a SQLCLR procedure which read the data from a XML column in a SQL table into an in-memory (table) using .net (System.Data) then used the .net System.Xml namespace to deserialize the xml, populated another in-memory table (in the shape of the destination table) and used the sqlbulkcopy to populate that destination SQL table with those parsed attributes I needed.
SQL Server is engineered for set-based operations... If ever I'm shredding/iterating (row-by-row) I tend to use SQLCLR as .Net is generally better at iterative/data-manipulative processing. An exception to my rule is when working with a little metadata for data-driven processes, cleanup routines where I may use a cursor.
I have a program in c# in VS that runs a mainform.
That mainform exports data to an SQL Database with stored procedures into tables. The data exported is a lot of data (600,000 + rows).
I have a problem tho. On my mainform I need to have a "database write out interval". This is a number of how many "rows" will be imported into the database.
My problem is however the steps on how to implement that interval. The mainform runs, and when the main program is done, the sql still takes IN data for another 5-10 minutes.
Therefore, if I close the mainform, the rest of the data will not me imported.
Do you professional programmers out there know a way where I can somehow communicate with SQL to only export data for a user-specified interval. T
his has to be done with my c# class.
I dont know where to begin.
I dont think a timer would be a good idea because differenct computers and cpu's perform differently. Any advice would be appreciated.
If the data is of a fixed format (ie, there are going to be the same columns for every row and its not going to change much), you should look at Bulk Insert. Its incredibly fast at inserting large numbers of rows.
The basics are you write your data out to a text file (ie, csv, but you can specify whatever delimiter you want), then execute a BULK INSERT command against the server. One of the arguments is the path to the file you wrote out. It's a bit of a pain to use because you have to write the file in a folder on the server (or a UNC path that the server has access to) which leads to configuring windows shares or setting up FTP on the server. It sounds like exactly what you want to use, though.
Here's the MSDN documentation on BULK INSERT:
http://msdn.microsoft.com/en-us/library/ms188365.aspx
Instead of exporting all of your data to SQL and then trying to abort or manage the load a a better process might be to split your load into smaller chunks (10,000 records or so) and check whether the user wants to continue after each load. This gives you a lot more flexibility and control over the load then dumping all 600,000 records to SQL and trying to manage the process.
Also what Tim Coker mentioned is spot on. Even if your stored proc is doing some data manipulation it is a lot faster to load the data via bulk insert and run a query after the load to do any work you have to do then to run all 600,000 records through the stored proc.
Like all the other comments before, i will suggest you to use BulkInsert. You will be amazed by how fast the performance is when it comes to large dataset and perhaps your concept about interval is no longer required. Inserting 100k of records may only take seconds.
Depends on how your code is written, ADO.NET has native support for BulkInsert through SqlBulkCopy, see the code below
http://www.knowdotnet.com/articles/bulkcopy_intro1.html
If you have been using Linq to db for your code, there are already some clever code written as extension method to the datacontext which transform the linq changeset into a dataset and internally use ADO.NET to achieve the bulk insert
http://blogs.microsoft.co.il/blogs/aviwortzel/archive/2008/05/06/implementing-sqlbulkcopy-in-linq-to-sql.aspx
Say I have a few tables in the MSSQL database, each with about 5-10 attributes. There are some simple associations between the tables, but each of the table have 500,000 to 1,000,000 rows.
There is an algorithm that runs on that data (all of it), so before running the algorithm, I have to retrieve all the data from the database. The algorithm does not change the data, only reads it, so I just need to retrieve the data.
I am using LINQ to SQL. To retrieve all the data takes about two minutes. What I want to know is whether the serialization to file and then deserialization (when needed) would actually load the data faster.
The data is about 200 MB, and I don't mind saving it to disk. So, would it be faster if the objects were deserialized from the file or by using LINQ 2 SQL DataContext?
Any experiences with this?
I would argue that LINQtoSQL may not be the best choice for this kind of application. When you are talking about so many objects, you incur quite some overhead creating object instances (your persistent classes).
I would choose a solution where a stored procedure retrieves only the necessary data via ADO.NET, the application stores it in memory (memory is cheap nowadays, 200MB should not be a problem) and the analyzing algorithm is run on the in-memory data.
I don't think you should store the data on file. In the end, your database is also simply one or more files that are read by the database engine. So you either
let the database engine read your data and you analyze it, or
let the database engine read your data, you write it to file, you read the file (reading the same data again, but now you do it yourself) and you analyze the data
The latter option involves a lot of overhead without any advantages as far as I can see.
EDIT: If your data changes very infrequently, you may consider preprocessing your data before analyzing and caching the preprocessed data somewhere (in the database or on the file system). This only makes sense if your preprocessed data can be analyzed (a lot) faster than the raw data. Maybe some preprocessing can be done in the database itself.
You should try to use ADO.NET directly without the LINQ to SQL layer on top of it, i.e. using an SqlDataReader to read the data.
If you work sequentially with the data, you can get the records from the reader when you need them without having to read them all into memory first.
If you have a process that operates on most of the data in a database... then that sounds like a job for a stored procedure. It won't be object oriented, but it will be a lot faster and less brittle.
Since you are doing this in C# and your database is MsSql (since you use Linq to Sql), could you not run your code in a managed stored procedure? That would allow you to keep your current code as it is, but loading the data would be much faster since the code was running in the sql server.
I need to upload a massive (16GB, 65+ million records) CSV file to a single table in a SQL server 2005 database. Does anyone have any pointers on the best way to do this?
Details
I am currently using a C# console application (.NET framework 2.0) to split the import file into files of 50000 records, then process each file. I upload the records into the database from the console application using the SqlBulkCopy class in batches of 5000. To split the files takes approximately 30 minutes, and to upload the entire data set (65+ million records) takes approximately 4.5 hours. The generated file size and the batch upload size are both configuration settings, and I am investigating increasing the value of both to improve performance. To run the application, we use a quad core server with 16GB RAM. This server is also the database server.
Update
Given the answers so far, please note that prior to the import:
The database table is truncated, and all indexes and constraints are dropped.
The database is shrunk, and disk space reclaimed.
After the import has completed:
The indexes are recreated
If you can suggest any different approaches, or ways we can improve the existing import application, I would appreciate it. Thanks.
Related Question
The following question may be of use to others dealing with this problem:
Potential Pitfalls of inserting millions of records into SQL Server 2005 from flat file
Solution
I have investigated the affect of altering batch size, and the size of the split files, and found that batches of 500 records, and split files of 200,000 records work best for my application. Use of the SqlBulkCopyOptions.TableLock also helped. See the answer to this question for further details.
I also looked at using a SSIS DTS package, and a BULK INSERT SQL script. The SSIS package appeared quicker, but did not offer me the ability to record invalid records, etc. The BULK INSERT SQL script whilst slower than the SSIS package, was considerably faster than the C# application. It did allow me to record errors, etc, and for this reason, I am accepting the BULK INSERT answer from ConcernedOfTunbridgeWells as the solution. I'm aware that this may not be the best answer for everyone facing this issue, but it answers my immediate problem.
Thanks to everyone who replied.
Regards, MagicAndi
BULK INSERT is run from the DBMS itself, reading files described by a bcp control file from a directory on the server (or mounted on it). Write an application that splits the file into smaller chunks, places them in an appropriate directory executes a wrapper that executes a series of BULK INSERTS. You can run several threads in parallel if necessary.
This is probably about as fast as a bulk load gets. Also, if there's a suitable partitioning key available in the bulk load file, put the staging table on a partition scheme.
Also, if you're bulk loading into a table with a clustered index, make sure the data is sorted in the same order as the index. Merge sort is your friend for large data sets.
Have you tried SSIS (SQL Server Integration Services).
The SqlBulkCopy class that you're already using is going to be your best bet. The best you can do from here in your c# code is experiment with your particular system and data to see what batch sizes work best. But you're already doing that.
Going beyond the client code, there might be some things you can do with the server to make the import run more efficiently:
Try setting the table and database size before starting the import to something large enough to hold the entire set. You don't want to rely on auto-grow in the middle of this.
Depending on how the data is sorted and any indexes one the table, you might do a little a better to drop any indexes that don't match the order in which the records are imported, and then recreate them after the import.
Finally, it's tempting to try running this in parallel, with a few threads doing bulk inserts at one time. However, the biggest bottleneck is almost certainly disk performance. Anything you can do to the physical server to improve that (new disks, san, etc) will help much more.
You may be able to save the step of splitting the files as follows:
Instantiate an IDataReader to read the values from the input CSV file. There are several ways to do this: the easiest is probably to use the Microsoft OleDb Jet driver. Google for this if you need more info - e.g. there's some info in this StackOverflow question.
An alternative method is to use a technique like that used by www.csvreader.com.
Instantiate a SqlBulkCopy object, set the BatchSize and BulkCopyTimeout properties to appropriate values.
Pass the IDataReader to SqlBulkCopy.WriteToServer method.
I've used this technique successfully with large files, but not as large as yours.
See this and this blog posts for a comparison.
It seems the best alternative is to use BulkInsert with the TABLOCK option set to true.
Have you tried using the Bulk Insert method in Sql Server?
Lately, I had to upload/import a lot of stuff, too (built a PHP script).
I decided to process them record-for-record.
Of course, it takes longer, but for me, the following points were important:
- easily pause the process
- better debugging
This is just a tip.
regards,
Benedikt
BULK INSERT is probably already the fastest way. You can gain additional performance by dropping indexes and constraints while inserting and reestablishing them later. The highest performance impact comes from clustered indexes.
Have you tried SQL Server Integration Services for this? It might be better able to handle such a large text file
Just to check, your inserting will be faster if there are no indexes on the table you are inserting into.
My scenario for things like that is:
Create SSIS Package on SQL server which using BLUK insert into sql,
Create stored procedure inside the DataBase to can run that Package from T-SQL code
After that send file for bluk insert to SQL server using FTP and call SSIS Package usinfg stored procedure