ORIGINAL QUESTION:
I have some code which looks like this:
for (int i = start_i; i <= i_s; i++)
{
var json2 = JObject.Parse(RequestServer("query_2", new List<JToken>(){json1["result"]}));
foreach (var data_1 in json2["result"]["data_1"])
{
var json3 = JObject.Parse(RequestServer("query_3", new List<JToken>(){data_1, 1}));
foreach (var data_2 in json3["result"]["data_2"])
{
var data_1 = data_2["id"];
var index = data_2["other"];
}
foreach (var other in json3["result"]["other"])
{
var data_3_1 = other["data_3"]["data_3_1"];
var data_4 = other["data_4"];
var data_5 = other["data_5"];
foreach (var data_3_1 in other["data_3"]["data_3_1"])
{
//Console.WriteLine(data_3_1); <- very fast
insert_data((string)data_3_1); <- very slow
}
}
}
}
This code was able to generate about 5000 WriteLines in less than a minute. However, I now want to insert that data into a database. When I try to do that, the code now takes much much longer to get through the 5000 sets of data.
My question is, how do I batch the database inserts into about 1000 inserts at a time, instead of doing one at a time. I have tried creating the insert statement using a stringbuilder which is fine, what I can't figure out is how to generate 1000 at a time. I have tried using for loops upto 1000, and then trying to break out of the foreach loop, before starting with the next 1000, but it just makes a big mess.
I have looked at questions like this example, but they are no good for my loop scenario. I know how to do bulk inserts at the sql level, I just can't seem to figure out how to generate the bulk sql inserts using the unique loop situation I have above using the those very specific loops in the example code.
The 5000 records was just a test run. The end code will have to deal with millions, if not billions of inserts. Based on rough calculations, the end result will use about 500GB of drive space when inserted into a database, so I will need to batch an optimum amount into RAM before inserting into the database.
UPDATE 1:
This is what happens in insert_data:
public static string insert_data(string data_3_1)
{
string str_conn = #"server=localhost;port=3306;uid=username;password=password;database=database";
MySqlConnection conn = null;
conn = new MySqlConnection(str_conn);
conn.Open();
MySqlCommand cmd = new MySqlCommand();
cmd.Connection = conn;
cmd.CommandText = "INSERT INTO database_table (data_3_1) VALUES (#data_3_1)";
cmd.Prepare();
cmd.Parameters.AddWithValue("#data_3_1", data_3_1);
cmd.ExecuteNonQuery();
cmd.Parameters.Clear();
return null;
}
You're correct that doing bulk inserts in batches can be a big throughput win. Here's why it's a win: When you do INSERT operations one at a time, the database server does an implicit COMMIT operation after every insert, and that can be slow. So, if you can wrap every hundred or so INSERTs in a single transaction, you'll reduce that overhead.
Here's an outline of how to do that. I'll try to put it in the context of your code, but you didn't show your MySQLConnection object or query objects, so this solution of mine necessarily will be incomplete.
var batchSize = 100;
var batchCounter = batchSize;
var beginBatch = new MySqlCommand("START TRANSACTION;", conn);
var endBatch = new MySqlCommand("COMMIT;", conn);
beginBatch.ExecuteNonQuery();
for (int i = start_i; i <= i_s; i++)
{
....
foreach (var data_1 in json2["result"]["data_1"])
{
...
foreach (var other in json3["result"]["other"])
{
...
foreach (var data_3_1 in other["data_3"]["data_3_1"])
{
//Console.WriteLine(data_3_1); <- very fast
/****************** batch handling **********************/
if ( --batchCounter <= 0) {
/* commit one batch, start the next */
endBatch.ExecuteNonQuery();
beginBatch.ExecuteNonQuery();
batchCounter = batchSize;
}
insert_data((string)data_3_1); <- very slow
}
}
}
}
/* commit the last batch. It's OK if it contains no records */
endBatch.ExecuteNonQuery();
If you want, you can try different values of batchSize to find a good value. But generally something like the 100 I suggest works well.
Batch sizes of 1000 are also OK. But the larger each transaction gets, the more server RAM it uses before it's committed, and the longer it might block other programs using the same MySQL server.
There's a nice and popular extension called MoreLinq that offers an extension method called Batch(int batchSize). To get an IEnumerable containing up to 1000 elements:
foreach (var upTo1000 in other["data_3"]["data_3_1"].Batch(1000))
{
// Build a query using the (up to) 1000 elements in upTo1000
}
The best approach for me was using LOAD DATA LOCAL INFILE statement. To make it work first you have to turn ON MySQL server parameter local_infile.
I used mysql2 package for NodeJS and query function:
db.query({
sql: "LOAD DATA LOCAL INFILE .......",
infileStreamFactory: <readable stream which provides your data in flat file format>
}, function(err, results) {....});
The trick is to provide a readable stream properly. By default, LOAD DATA expects tab delimited text file. Also LOAD DATA expects some file name and in you case if you provide a stream then file name can be arbitrary string.
Related
I wrote a program some time ago that delimits and reads in pretty big text files. The program works but the problem is it basically freezes the computer and takes long time to finish. On average each text file has around 10K to 15K lines, and each line represents a new row in a SQL table.
Way my program works is I first read all of the lines (this is where delimiting happens) and store them in array, after that I go through each array element and insert them into SQL table. This is all done at once and I suspect is eating up to much memory which is causing the program to freeze the computer.
Here is my code for reading file:
private void readFile()
{
//String that will hold each line read from the file
String line;
//Instantiate new stream reader
System.IO.StreamReader file = new System.IO.StreamReader(txtFilePath.Text);
try
{
while (!file.EndOfStream)
{
line = file.ReadLine();
if (!string.IsNullOrWhiteSpace(line))
{
if (this.meetsCondition(line))
{
badLines++;
continue;
} // end if
else
{
collection.readIn(line);
counter++;
} // end else
} // end if
} // end while
file.Close();
} // end try
catch (Exception exceptionError)
{
//Placeholder
}
Code for inserting:
for (int i = 0; i < counter; i++)
{
//Iterates through the collection array starting at first index and going through until the end
//and inserting each element into our SQL Table
//if (!idS.Contains(collection.getIdItems(i)))
//{
da.InsertCommand.Parameters["#Id"].Value = collection.getIdItems(i);
da.InsertCommand.Parameters["#Date"].Value = collection.getDateItems(i);
da.InsertCommand.Parameters["#Time"].Value = collection.getTimeItems(i);
da.InsertCommand.Parameters["#Question"].Value = collection.getQuestionItems(i);
da.InsertCommand.Parameters["#Details"].Value = collection.getDetailsItems(i);
da.InsertCommand.Parameters["#Answer"].Value = collection.getAnswerItems(i);
da.InsertCommand.Parameters["#Notes"].Value = collection.getNotesItems(i);
da.InsertCommand.Parameters["#EnteredBy"].Value = collection.getEnteredByItems(i);
da.InsertCommand.Parameters["#WhereReceived"].Value = collection.getWhereItems(i);
da.InsertCommand.Parameters["#QuestionType"].Value = collection.getQuestionTypeItems(i);
da.InsertCommand.Parameters["#AnswerMethod"].Value = collection.getAnswerMethodItems(i);
da.InsertCommand.Parameters["#TransactionDuration"].Value = collection.getTransactionItems(i);
da.InsertCommand.ExecuteNonQuery();
//}
//Updates the progress bar using the i in addition to 1
_worker.ReportProgress(i + 1);
} // end for
If you can map your collection to a DataTable then you could use an SqlBulkCopy to import your data. SqlBulkCopy is the fastest way to import data from .Net into SqlServer.
Use SqlBulkCopy class for bulk inserts.
http://msdn.microsoft.com/en-us/library/system.data.sqlclient.sqlbulkcopy.aspx
You will cut down the time to mere seconds.
+1 for SqlBulkCopy as others have stated, but be aware that it requires INSERT permission. If you work in a strictly controlled environment, as I do, where you aren't allowed to use dynamic SQL an alternative approach is to have your stored proc use Table-Valued parameters. That way you can still pass in chunks of records and have the proc do the actual inserting.
As an example how to use the functionaloty of the SqlBulkCopy class, (It is just pseudocode to render the idea)
First change your collection class to host an internal DataTable, and in the constructor define the schema used by your readIn method
public class MyCollection
{
private DataTable loadedData = null;
public MyCollection()
{
loadedData = new DataTable();
loadedData.Columns.Add("Column1", typeof(string));
.... and so on for every field expected
}
// A property to return the collected data
public DataTable GetData
{
get{return loadedData;}
}
public void readIn(string line)
{
// split the line in fields
DataRow r = loadedData.NewRow();
r["Column1"] = splittedLine[0];
.... and so on
loadedData.Rows.Add(r);
}
}
Finally the code that upload the data to your server
using (SqlConnection connection = new SqlConnection(connectionString))
{
connection.Open();
using (SqlBulkCopy bulkCopy = new SqlBulkCopy(connection))
{
bulkCopy.DestinationTableName = "destinationTable";
try
{
bulkCopy.WriteToServer(collection.GetData());
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
}
}
}
As mentioned, using SqlBulkCopy will be faster than inserting one-by-one, but there are other things that you could look at:
Is there a clustered index on the table? If so will you be inserting rows with values in the middle of that index? It's much more efficient to add values at the end of a clustered index since otherwise it will have to rearrange data to insert in in the middle (this is only for CLUSTERED indexes). On example I've seen us using SSN as a clustered primary key. Since SSNs will be distributed randomly, you are rearranging the physical structure on virtually every insert. Having a date as part of the clustered key may be OK if you are MOSTLY inserting data at the end (e.g. adding daily records)
Are there a lot of indexes on that table? it may be more efficient to drop the indexes, add the data, and re-add the indexes after the inserts. (or just drop indexes you don't need)
I'm really struggling to iron out this issue. When I use the following code to update my database for large numbers of records it runs extremely slow. I've got 500,000 records to update which takes nearly an hour. During this operation, the journal file grows slowly with little change on the main SQLite db3 file - is this normal?
The operation only seems to be a problem when I have large numbers or records to update - it runs virtually instantly on smaller numbers of records.
Some other operations are performed on the database prior to this code running so could they be some how tying up the database? I've tried to ensure that all other connections are closed properly.
Thanks for any suggestions
using (SQLiteConnection sqLiteConnection = new SQLiteConnection("Data Source=" + _case.DatabasePath))
{
sqLiteConnection.Open();
using (SQLiteCommand sqLiteCommand = new SQLiteCommand("begin", sqLiteConnection))
{
sqLiteCommand.ExecuteNonQuery();
sqLiteCommand.CommandText = "UPDATE CaseFiles SET areaPk = #areaPk, KnownareaPk = #knownareaPk WHERE mhash = #mhash";
var pcatpk = sqLiteCommand.CreateParameter();
var pknowncatpk = sqLiteCommand.CreateParameter();
var pmhash = sqLiteCommand.CreateParameter();
pcatpk.ParameterName = "#areaPk";
pknowncatpk.ParameterName = "#knownareaPk";
pmhash.ParameterName = "#mhash";
sqLiteCommand.Parameters.Add(pcatpk);
sqLiteCommand.Parameters.Add(pknowncatpk);
sqLiteCommand.Parameters.Add(pmhash);
foreach (CatItem CatItem in _knownFiless)
{
if (CatItem.FromMasterHashes == true)
{
pcatpk.Value = CatItem.areaPk;
pknowncatpk.Value = CatItem.areaPk;
pmhash.Value = CatItem.mhash;
}
else
{
pcatpk.Value = CatItem.areaPk;
pknowncatpk.Value = null;
pmhash.Value = CatItem.mhash;
}
sqLiteCommand.ExecuteNonQuery();
}
sqLiteCommand.CommandText = "end";
sqLiteCommand.ExecuteNonQuery();
sqLiteCommand.Dispose();
sqLiteConnection.Close();
}
sqLiteConnection.Close();
}
The first thing to ensure that you have an index on mhash.
Group commands into batches.
Use more than one thread.
Or [inserted]
Bulk import the records to a temporary table. Create an index on the mhash column. Perform a single update statement to update the records.
You need to wrap everything inside a transaction otherwise I believe SQLite will create and commit one for you for every update ... hence the slowness. You clearly know that looking at your code but I am not sure using "Begin" and "End" commands achieve the same result here, you might end up with empty transaction at start and finish instead of one wrapping everything. Try something like this instead just in case:
using (SQLiteTransaction mytransaction = myconnection.BeginTransaction())
{
using (SQLiteCommand mycommand = new SQLiteCommand(myconnection))
{
SQLiteParameter myparam = new SQLiteParameter();
mycommand.CommandText = "YOUR QUERY HERE";
mycommand.Parameters.Add(myparam);
foreach (CatItem CatItem in _knownFiless)
{
...
mycommand.ExecuteNonQuery();
}
}
mytransaction.Commit();
}
This part is most certainly your problem.
foreach (CatItem CatItem in _knownFiless)
{
....
sqLiteCommand.ExecuteNonQuery();
}
You are looping a List(?) and executing a query against the database. That is not a good way to do it. Because database calls are quite expensive. So you might consider using another way of updating these items.
The SQL code appears to be okay. The C# code is not wrong, but it has some redundancy (explicit close/dispose is not needed since you're using a using already).
There is a for loop on _knownFiless (intended with double s?), could that run slowly possibly? It is unusual to run a query in a for loop against the DB, rather you should create a query with the respective set of parameters. Consider that (especially without an index on the hash) you will perform n * m operations (n being the run count of the for loop, m being the table size).
Considering that m is around 500k, and assuming that m = n you will get 250,000,000,000 operations. That may well last an hour.
Former connections or operations should have no effect as far as I know.
You should also ensure that the internal structure of the database is not causing problems. Is there a compound index that is affected from this operation? Any foreign keys / complex contraints?
can someone help me speed this up.
I have a dataset (from a csv file) called dsresult and I want to pump it into a firebird table.
Currently I am doing it 1 row at a time, but I would prefer to do this in batches of 500 rows.
I am using the firebird.net provider
string connectionString = "ServerType=1;User=SYSDBA;Password=masterkey;Dialect=3;Database=MYDB.FDB";
string sql = "INSERT INTO POSTIN (NUMID, CHANGE, PLACENAME, BOXCODE, STRCODE, TOWN) VALUES (#NUMID, #CHANGE, #PLACENAME, #BOXCODE, #STRCODE, #TOWN)";
FbConnection conn = new FbConnection(connectionString)
FbCommand command = new FbCommand(sql, conn);
foreach (DataRow r in dsResult.Tables[0].Rows)
{
command.Parameters.AddWithValue("#NUMID", r["NUMID"]);
command.Parameters.AddWithValue("#CHANGE", r["CHANGE"]);
command.Parameters.AddWithValue("#PLACENAME", r["PLACENAME"]);
command.Parameters.AddWithValue("#BOXCODE", r["BOXCODE"]);
command.Parameters.AddWithValue("#STRCODE", r["STRCODE"]);
command.Parameters.AddWithValue("#TOWN", r["TOWN"]);
command.ExecuteNonQuery();
}
it takes aaaaaaaaaaages to run.
in delphi i would have just jused cachedupdates.
post 500 records at a time
and commit on the 500th
Thanks
try something like this:
using(FbConnection c = new FbConnection(csb.ToString()))
{
FbBatchExecution fbe = new FbBatchExecution(c);
//loop through your commands here
{
fbe.SqlStatements.Add(cmd);
}
fbe.Execute();
}
Firebird's wire protocol doesn't support sending more commands in one batch (and in one roundtrip). Probably best idea is to use EXECUTE BLOCK (aka anonymous stored procedure) and send inserts there.
For example:
execute block
as
begin
insert into ...;
insert into ...;
...
end
and execute this.
BTW the FbBatchExecution will send one command a time as well.
You should only add the parameters once, and only change the values off them in the loop so something like:
create the parameters once:
command.Parameters.Add("#NUMID", FbDbType.Int)
....
in the loop do:
foreach (DataRow r in dsResult.Tables[0].Rows)
{
command.Parameters["#NUMID"].Value = r["NUMID"];
....
}
this should really speed things up.
I am doing bulk insert in syabse database by grouping insert query and sending it to database in batch where size of batch is configurable, the code looks somewhat like this
public static void InsertModelValueInBulk(DataSet modelValueData, int clsaId)
{
int batchSize = Convert.ToInt32(ConfigurationManager.AppSettings["BatchSize"].ToString());
IList<string> queryBuffer = new List<string>();
using (var connection = GetAseConnection())
{
connection.Open();
var tran = connection.BeginTransaction();
try
{
for (int i = 0; i < modelValueData.Tables[0].Rows.Count; i++)
{
var insertItem = string.Format(#"select '{0}',{1},{2},{3},'{4}','{5}','{6}',{7}", row["ModelValueID"], Convert.ToInt32(row["StockModelID"]), Convert.ToInt32(row["ModelItemID"]),
fyeStr, row["Period"], value, row["UpdatedUser"], clsaId);
queryBuffer.Add(insertItem);
if (queryBuffer.Count % (batchSize) == 0 && queryBuffer.Count > 0)
{
var finalQuery = #"INSERT INTO InsertTable (ModelValueID, StockModelID, ModelItemID, FYE, Period, Value, UpdatedUser,id)
" + String.Join(" union ", queryBuffer.ToArray<string>());
using (var cmd = new AseCommand(finalQuery, connection, tran))
{
cmd.ExecuteNonQuery();
}
queryBuffer.Clear();
}
}
tran.Commit();
}
catch
{
tran.Rollback();
throw;
}
finally
{
tran.Dispose();
}
}
}
using this the performance observed for batch size vs time taken to insert 20000 forms a J curve, sample data is somewhat like
batch size 10 => Operation completes in 30 sec, when batch size is 50 => 20 sec, 100=>10 sec, 200=>20 sec, 500 30 sec, 1000=>1 min.
Would like to understand what is reason behind this J curve. Is it something to do with app server memory or some database server setting or its something else? What makes 100 optimum and can this be tweaked further?
BULK insert locks the table for the duration of the batch size. Locks have a basic overhead, so small batches won't benefit nearly as much, but do let other operations happen against the table in-between batches.
So larger batches are good, to a point. Because it's a transaction, the data is not committed until the current batch is complete. This means writing to the log file. Really large batches will cause the log to grow, which is IO intensive, it also increases contention as more of your log will be in use.
Something along those lines.
edit: Two other things
1) Use parameterized inputs
2) If you don't do #1, "union" causes a distinct. Use "union all"
I see quite a feww Issues with you existing code.. for example.. on your Commit I would not assume that Commits would always be successful..
I would wrap all code that could have the potential to fail or explode around a try catch Commits, Rollbacks cmd.Execute
I would look at my Select statement and personally I would create a stored procedure and if you can't do that I would make the select string a const.
I would name my transactions personally.. but that's up to you
does this line have the potential of changing during every method call..
int batchSize = Convert.ToInt32(ConfigurationManager.AppSettings["BatchSize"].ToString());
if not I would make it a static call and not call it everytime you go into the method
try to refactor your code .. it's starting to look a bit confusing to follow..
I am using C# to import a CSV with 6-8million rows.
My table looks like this:
CREATE TABLE [Data] ([ID] VARCHAR(100) NULL,[Raw] VARCHAR(200) NULL)
CREATE INDEX IDLookup ON Data(ID ASC)
I am using System.Data.SQLite to do the import.
Currently to do 6 millions rows its taking 2min 55 secs on a Windows 7 32bit, Core2Duo 2.8Ghz & 4GB RAM. That's not too bad but I was just wondering if anyone could see a way of importing it quicker.
Here is my code:
public class Data
{
public string IDData { get; set; }
public string RawData { get; set; }
}
string connectionString = #"Data Source=" + Path.GetFullPath(AppDomain.CurrentDomain.BaseDirectory + "\\dbimport");
System.Data.SQLite.SQLiteConnection conn = new System.Data.SQLite.SQLiteConnection(connectionString);
conn.Open();
//Dropping and recreating the table seems to be the quickest way to get old data removed
System.Data.SQLite.SQLiteCommand command = new System.Data.SQLite.SQLiteCommand(conn);
command.CommandText = "DROP TABLE Data";
command.ExecuteNonQuery();
command.CommandText = #"CREATE TABLE [Data] ([ID] VARCHAR(100) NULL,[Raw] VARCHAR(200) NULL)";
command.ExecuteNonQuery();
command.CommandText = "CREATE INDEX IDLookup ON Data(ID ASC)";
command.ExecuteNonQuery();
string insertText = "INSERT INTO Data (ID,RAW) VALUES(#P0,#P1)";
SQLiteTransaction trans = conn.BeginTransaction();
command.Transaction = trans;
command.CommandText = insertText;
Stopwatch sw = new Stopwatch();
sw.Start();
using (CsvReader csv = new CsvReader(new StreamReader(#"C:\Data.txt"), false))
{
var f = csv.Select(x => new Data() { IDData = x[27], RawData = String.Join(",", x.Take(24)) });
foreach (var item in f)
{
command.Parameters.AddWithValue("#P0", item.IDData);
command.Parameters.AddWithValue("#P1", item.RawData);
command.ExecuteNonQuery();
}
}
trans.Commit();
sw.Stop();
Debug.WriteLine(sw.Elapsed.Minutes + "Min(s) " + sw.Elapsed.Seconds + "Sec(s)");
conn.Close();
This is quite fast for 6 million records.
It seems that you are doing it the right way, some time ago I've read on sqlite.org that when inserting records you need to put these inserts inside transaction, if you don't do this your inserts will be limited to only 60 per second! That is because each insert will be treated as a separate transaction and each transaction must wait for the disk to rotate fully. You can read full explanation here:
http://www.sqlite.org/faq.html#q19
Actually, SQLite will easily do 50,000 or more INSERT statements per second on an average desktop computer. But it will only do a few dozen transactions per second. Transaction speed is limited by the rotational speed of your disk drive. A transaction normally requires two complete rotations of the disk platter, which on a 7200RPM disk drive limits you to about 60 transactions per second.
Comparing your time vs Average stated above: 50,000 per second => that should take 2m 00 sec. Which is only little faster than your time.
Transaction speed is limited by disk drive speed because (by default) SQLite actually waits until the data really is safely stored on the disk surface before the transaction is complete. That way, if you suddenly lose power or if your OS crashes, your data is still safe. For details, read about atomic commit in SQLite..
By default, each INSERT statement is its own transaction. But if you surround multiple INSERT statements with BEGIN...COMMIT then all the inserts are grouped into a single transaction. The time needed to commit the transaction is amortized over all the enclosed insert statements and so the time per insert statement is greatly reduced.
There is some hint in next paragraph that you could try to speed up the inserts:
Another option is to run PRAGMA synchronous=OFF. This command will cause SQLite to not wait on data to reach the disk surface, which will make write operations appear to be much faster. But if you lose power in the middle of a transaction, your database file might go corrupt.
I always thought that SQLite was designed for "simple things", 6 millions of records seems to me is a job for some real database server like MySQL.
Counting records in a table in SQLite with so many records can take long time, just for your information, instead of using SELECT COUNT(*), you can always use SELECT MAX(rowid) which is very fast, but is not so accurate if you were deleting records in that table.
EDIT.
As Mike Woodhouse stated, creating the index after you inserted the records should speed up the whole thing, that is a common advice in other databases, but can't say for sure how it works in SQLite.
One thing you might try is to create the index after the data has been inserted - typically it's much faster for databases to build indexes in a single operation than to update it after each insert (or transaction).
I can't say that it'll definitely work with SQLite, but since it only needs two lines to move it's worth trying.
I'm also wondering if a 6 million row transaction might be going too far - could you change the code to try different transaction sizes? Say 100, 1000, 10000, 100000? Is there a "sweet spot"?
You can gain quite some time when you bind your parameters in the following way:
...
string insertText = "INSERT INTO Data (ID,RAW) VALUES( ? , ? )"; // (1)
SQLiteTransaction trans = conn.BeginTransaction();
command.Transaction = trans;
command.CommandText = insertText;
//(2)------
SQLiteParameter p0 = new SQLiteParameter();
SQLiteParameter p1 = new SQLiteParameter();
command.Parameters.Add(p0);
command.Parameters.Add(p1);
//---------
Stopwatch sw = new Stopwatch();
sw.Start();
using (CsvReader csv = new CsvReader(new StreamReader(#"C:\Data.txt"), false))
{
var f = csv.Select(x => new Data() { IDData = x[27], RawData = String.Join(",", x.Take(24)) });
foreach (var item in f)
{
//(3)--------
p0.Value = item.IDData;
p1.Value = item.RawData;
//-----------
command.ExecuteNonQuery();
}
}
trans.Commit();
...
Make the changes in sections 1, 2 and 3.
In this way parameter binding seems to be quite a bit faster.
Especially when you have a lot of parameters, this method can save quite some time.
I did a similar import, but I let my c# code just write the data to a csv first and then ran the sqlite import utility. I was able to import over 300million records in a matter of maybe 10 minutes this way.
Not sure if this can be done directly from c# or not though.