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How to run multiple queries against SQL Server?

I have read and implemented several different versions of Microsofts suggested methods for querying a SQL Server database. In all that I have read, each query is surrounded by a using statement, e.g. In some method DoQuery:
List<List<string>> DoQuery(string cStr, string query)
{
using(SqlConnection c = new SqlConnection(cStr))
{
c.Open();
using(SqlCommand cmd = new SqlCommand(queryStr, c))
{
using(SqlDataReader reader = cmd.ExecuteReader())
{
while (reader.Read() )
{
...
//read columns and put into list to return
}
// close all of the using blocks
}
}
}
// return the list of rows containing the list of column values.
}
I need to run this code several hundreds of times for different query strings against the same database. It seems that creating a new connection each time would be inefficient and dropping it each time wasteful.
How should I structure this so that it is efficient? When I tried not using a using block and passing the connection into the DoQuery method, I got messages about the connection had not been closed. If I closed it after the query, then I got messages about it wasn't open.
I'm also trying to improve this because I keep getting somewhat random
IOException: Unable to read data from the transport connection: Operation on non-blocking socket would block.
I'm the only user of the database at this time and I'm not doing anything in multiple threads or async, etc. Just looping through query strings and running DoQuery on them.
Could my structure be part of that problem, i.e. not releasing the resources fast enough and thereby seeing the connection blocked?
I'm stuck here on efficiency and this blocking problem. Thanks in advance.

As it turns out, the query structure was fine and the queries were fine. The problem was that I had an ‘order by X desc’ on each query and that column was not indexed. This caused a full table scan to order the rows even if only returning 2. The table has about 3 million rows and I thought it could handle that better than it does. It timed out with 360 second connection timeout! I indexed the column and no more ‘blocking’ nonsense, which BTW, is a horrible message to return when it was actually a timeout. The queries now run fine if I index every column that appears in a where clause.

What is the best way to load huge result set in memory?

I am trying to load 2 huge resultsets(source and target) coming from different RDBMS but the problem with which i am struggling is getting those 2 huge result set in memory.
Considering below are the queries to pull data from source and target:
Sql Server -
select Id as LinkedColumn,CompareColumn from Source order by LinkedColumn
Oracle -
select Id as LinkedColumn,CompareColumn from Target order by LinkedColumn
Records in Source : 12377200
Records in Target : 12266800
Following are the approaches i have tried with some statistics:
1) open data reader approach for reading source and target data:
Total jobs running in parallel = 3
Time taken by Job1 = 01:47:25
Time taken by Job1 = 01:47:25
Time taken by Job1 = 01:48:32
There is no index on Id Column.
Major time is spent here:
var dr = command.ExecuteReader();
Problems:
There are timeout issues also for which i have to kept commandtimeout to 0(infinity) and it is bad.
2) Chunk by chunk reading approach for reading source and target data:
Total jobs = 1
Chunk size : 100000
Time Taken : 02:02:48
There is no index on Id Column.
3) Chunk by chunk reading approach for reading source and target data:
Total jobs = 1
Chunk size : 100000
Time Taken : 00:39:40
Index is present on Id column.
4) open data reader approach for reading source and target data:
Total jobs = 1
Index : Yes
Time: 00:01:43
5) open data reader approach for reading source and target data:
Total jobs running in parallel = 3
Index : Yes
Time: 00:25:12
I observed that while having an index on LinkedColumn does improve performance, the problem is we are dealing with a 3rd party RDBMS table which might not have an index.
We would like to keep database server as free as possible so data reader approach doesn't seem like a good idea because there will be lots of jobs running in parallel which will put so much pressure on database server which we don't want.
Hence we want to fetch records in the resource memory from source to target and do 1 - 1 records comparison to keep the database server free.
Note: I want to do this in my c# application and don't want to use SSIS or Linked Server.
Update:
Source Sql Query Execution time in sql server management studio: 00:01:41
Target Sql Query Execution time in sql server management studio:00:01:40
What will be the best way to read huge result set in memory?
Code:
static void Main(string[] args)
{
// Running 3 jobs in parallel
//Task<string>[] taskArray = { Task<string>.Factory.StartNew(() => Compare()),
//Task<string>.Factory.StartNew(() => Compare()),
//Task<string>.Factory.StartNew(() => Compare())
//};
Compare();//Run single job
Console.ReadKey();
}
public static string Compare()
{
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
var srcConnection = new SqlConnection("Source Connection String");
srcConnection.Open();
var command1 = new SqlCommand("select Id as LinkedColumn,CompareColumn from Source order by LinkedColumn", srcConnection);
var tgtConnection = new SqlConnection("Target Connection String");
tgtConnection.Open();
var command2 = new SqlCommand("select Id as LinkedColumn,CompareColumn from Target order by LinkedColumn", tgtConnection);
var drA = GetReader(command1);
var drB = GetReader(command2);
stopwatch.Stop();
string a = stopwatch.Elapsed.ToString(#"d\.hh\:mm\:ss");
Console.WriteLine(a);
return a;
}
private static IDataReader GetReader(SqlCommand command)
{
command.CommandTimeout = 0;
return command.ExecuteReader();//Culprit
}

There is nothing (I know of) faster than a DataReader for fetching db records.
Working with large databases comes with its challenges, reading 10 million records in under 2 seconds is pretty good.
If you want faster you can:
jdwend's suggestion:
Use sqlcmd.exe and the Process class to run query and put results into a csv file and then read the csv into c#. sqlcmd.exe is designed to archive large databases and runs 100x faster than the c# interface. Using linq methods are also faster than the SQL Client class
Parallize your queries and fetch concurrently merging results: https://shahanayyub.wordpress.com/2014/03/30/how-to-load-large-dataset-in-datagridview/
The easiest (and IMO the best for a SELECT * all) is to throw hardware at it:
https://blog.codinghorror.com/hardware-is-cheap-programmers-are-expensive/
Also make sure you're testing on the PROD hardware, in release mode as that could skew your benchmarks.

This is a pattern that I use. It gets the data for a particular record set into a System.Data.DataTable instance and then closes and disposes all un-managed resources ASAP. Pattern also works for other providers under System.Data include System.Data.OleDb, System.Data.SqlClient, etc. I believe the Oracle Client SDK implements the same pattern.
// don't forget this using statements
using System.Data;
using System.Data.SqlClient;
// here's the code.
var connectionstring = "YOUR_CONN_STRING";
var table = new DataTable("MyData");
using (var cn = new SqlConnection(connectionstring))
{
cn.Open();
using (var cmd = cn.CreateCommand())
{
cmd.CommandText = "Select [Fields] From [Table] etc etc";
// your SQL statement here.
using (var adapter = new SqlDataAdapter(cmd))
{
adapter.Fill(table);
} // dispose adapter
} // dispose cmd
cn.Close();
} // dispose cn
foreach(DataRow row in table.Rows)
{
// do something with the data set.
}

I think I would deal with this problem in a different way.
But before lets make some assumptions:
According to your question description, you will get data from SQL Server and Oracle
Each query will return a bunch of data
You do not specify what is the point of getting all that data in memory, neither the use of it.
I assume that the data you will process is going to be used multiple times and you will not repeat both queries multiple times.
And whatever you will do with the data, probably is not going to be displayed to the user all at the same time.
Having these foundation points I would process the following:
Think at this problem as a data processing
Have a third database or some other place with auxiliar Database tables where you can store all the result of the 2 queries.
To avoid timeouts or so, try to obtain the data using pagging (get thousands at a time) and save then in these aux DB tables, and NOT in "RAM" memory.
As soon as your logic completes all the data loading (import migration), then you can start processing it.
Data processing is a key point of database engines, they are efficient and lots of evolution during many years, do don't spend time reinventing the wheel. Use some Stored procedure to "crunch/process/merge" of the 2 auxiliary tables into only 1.
Now that you have all "merged" data in a 3th aux table, now you can use it to display or something else you need to use it.

If you want to read it faster, you must use original API to get the data faster. Avoid framework like linq and do rely on DataReader that one. Try to check weather you need something like dirty read (with(nolock) in sql server).
If your data is very huge, try to implement partial read. Something like making index to your data. Maybe you can put condition where date from - to until everything selected.
After that you must consider using Threading in your system to parallelize the flow. Actually 1 thread to get from job 1, another thread to get from job 2. This one will cut lot of time.

Technicalities aside, I think there is a more fundamental problem here.
select [...] order by LinkedColumn
I does observe that while having index on LinkedColumn does improve performance but the problem is we are dealing with 3rd party RDBMS tables which might have index or might not.
We would like to keep database server as free as possible
If you cannot ensure that the DB has a tree based index on that column, it means the DB will be quite busy sorting your millions of elements. It's slow and resource hungry. Get rid of the order by in the SQL statement and perform it on application side to get results faster and reduce load on DB ...or ensure the DB has such an index!!!
...depending if this fetching is a common or a rare operation, you'll want to either enforce a proper index in the DB, or just fetch it all and sort it client side.

I had a similar situation many years ago. Before I looked at the problem it took 5 days running continuously to move data between 2 systems using SQL.
I took a different approach.
We extracted the data from the source system into just a small number of files representing a flattened out data model and arranged the data in each file so it all naturally flowed in the proper sequence as we read from the files.
I then wrote a Java program that processed these flattened data files and produced individual table load files for the target system. So, for example, the source extract had less than a dozen data files from the source system which turned into 30 to 40 or so load files for the target database.
That process would run in just a few minutes and I incorporated full auditing and error reporting and we could quickly spot problems and discrepancies in the source data, get them fixed, and run the processor again.
The final piece of the puzzle was a multi-threaded utility I wrote that performed a parallel bulk load on each load file into the target Oracle database. This utility created a Java process for each table and used Oracle's bulk table load program to quickly push the data into the Oracle DB.
When all was said and done that 5 day SQL-SQL transfer of millions of records turned into just 30 minutes using a combination of Java and Oracle's bulk load capabilities. And there were no errors and we accounted for every penny of every account that was transferred between systems.
So, maybe think outside the SQL box and use Java, the file system, and Oracle's bulk loader. And make sure you're doing your file IO on solid state hard drives.

If you need to process large database result sets from Java, you can opt for JDBC to give you the low level control required. On the other hand, if you are already using an ORM in your application, falling back to JDBC might imply some extra pain. You would be losing features such as optimistic locking, caching, automatic fetching when navigating the domain model and so forth. Fortunately most ORMs, like Hibernate, have some options to help you with that. While these techniques are not new, there are a couple of possibilities to choose from.
A simplified example; let's assume we have a table (mapped to class "DemoEntity") with 100.000 records. Each record consists of a single column (mapped to the property "property" in DemoEntity) holding some random alphanumerical data of about ~2KB. The JVM is ran with -Xmx250m. Let's assume that 250MB is the overall maximum memory that can be assigned to the JVM on our system. Your job is to read all records currently in the table, doing some not further specified processing, and finally store the result. We'll assume that the entities resulting from our bulk operation are not modified

C# How do I prevent data loss from crashes during a long running query?

I have the following code that takes about an hour to run through a few hundred thousand rows:
public void Recording(int rowindex)
{
using (OleDbCommand cmd = new OleDbCommand())
{
try
{
using (OleDbConnection connection = new OleDbConnection(Con))
{
cmd.Connection = connection;
connection.Open();
using (OleDbTransaction Scope = connection.BeginTransaction(SD.IsolationLevel.ReadCommitted))
{
try
{
string Query = #"UPDATE [" + SetupAction.currentTable + "] set Description=#Description, Description_Department=#Description_Department, Accounts=#Accounts where ID=#ID";
cmd.Parameters.AddWithValue("#Description", VirtualTable.Rows[rowindex][4].ToString());
cmd.Parameters.AddWithValue("#Description_Department", VirtualTable.Rows[rowindex][18].ToString());
cmd.Parameters.AddWithValue("#Accounts", VirtualTable.Rows[rowindex][22].ToString());
cmd.Parameters.AddWithValue("#ID", VirtualTable.Rows[rowindex][0].ToString());
cmd.CommandText = Query;
cmd.Transaction = Scope;
cmd.ExecuteNonQuery();
Scope.Commit();
}
catch (OleDbException odex)
{
MessageBox.Show(odex.Message);
Scope.Rollback();
}
}
}
}
catch (OleDbException ex)
{
MessageBox.Show("SQL: " + ex);
}
}
}
It works as I expect it to, however today my program crashed while running the query (in a for loop where rowindex is the index of a datatable), the computer crashed, and when I rebooted the program, it said:
Multi-step OleDB operation generated errors: followed by my connection string.
What happened is that database is entirely uninteractable, even microsoft access's recovery methods can't seem to help out here.
I've read that this may be caused when the data structure of the database is altered from what it expected it to be. My question is, how do I prevent this, since I can't really detect whether my program stopped functioning all of a sudden.
There could be a way for me to restructure it somehow, maybe there's a function I don't know about. Perhaps it is sending something of an empty query when the crash happens, but I don't know how to stop it.

The Jet/ACE database engine already attempts to avoid corruption and to automatically recover from catastrophic events (lost connections, computer crashing). Transactions can further protected against inconsistent data by committing (or discarding) multiple operations altogether. But eventually there may be some coincidental system failure which could terminate an operation at some critical write position, thereby creating critical inconsistencies in the database file. Making regular and timely backups is part of an overall solution. For very long operations it might be worth making an automated copy of the entire database file prior to the operation.
Otherwise, an extreme alternative is to
Create a second intermediate database into which all data is first inserted. (Only needs to be done once.)
In this intermediate database, create linked tables to relevant tables in the permanent, working database.
Also in the intermediate database, create an indexed local table that mirrors the linked table structure into which data will be inserted. OR if the intermediate database and table already exist, clear the local table (i.e. delete all rows).
Have your current software insert into the local intermediate table.
Run a single query which then updates the linked table from the temporary table. Wrap that update in a transaction.
Here's where the linked table has the benefit that it can be referenced in an SQL query just like any local table. You only have to explicitly open the intermediate data. In other words, just perform a simple query like UPDATE LocalTable INNER JOIN LinkedTable ON LocalTable.UpdateID = LinkedTable.ID SET LinkedTable.Data = LocalTable.Data
The benefit to this process is that the single query that updates one Access table from another can be very fast, possibly much faster than the multiple update operations in your code. This could reduce the likelihood that errors in your update code will negatively effect your database. This of course doesn't completely eliminate the random computer crash that can effect the database, but reducing the time that multiple connections and update queries are executed might make it less likely.

I think your catch block is wrong, because if you get an exception other than OleDbException, you will not roll back the transaction
try
{
// ...
Scope.Commit();
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
Scope.Rollback();
}
That is, Exception instead of OleDbException. Exceptions could come from anywhere and not necessarily Ole DB, and you still want to roll back everything you've done so far in that case.
That being said, if you have a few hundred thousand rows, I would seriously consider batching the update, and processing just a few thousand per iteration with a transaction per iteration
In terms of transactional behavior, the main question would be: Do you really want to roll back everything you have updated so far in case of failure, or just retry/continue where you left off? If answer is that you want to retry/continue then you will likely want to create a BatchUpdateTask table or similar... with all the information you need for each iteration

Several inserts performance c#

I´ve to do several inserts into one table. Now, I´ve a list of objects that i iterate, and for each object i do an ExecuteNonQuery with the insert statement.
I want to know if there are a faste way to do this.
In this way, inserting 800 registers takes a couple of minutes.
I´ve used in Java the executeBatch method, who is for this propose, is there anything similar in c#.
Regards !

Inserts are [relatively] fast/cheap. Commits are slow/expensive.
Unless it is a high latency connection, multiple (as in, hundreds of) insert statements should be just fine.
"...takes a couple minutes..." sounds like transactions are not being used (and thus there are likely 800 commits -- ouch!). One of the easiest ways to control transactions in C# is to use a TransactionScope:
using (var connection = ...)
using (var tx = new TransactionScope()) {
foreach (var row in rows) {
// insert row
}
// commit all at once
tx.Complete();
}
I would only consider approaches if limiting the number of transactions (and thus commits) is not sufficient to meet functional requirements.
Happy coding.

Your statement could be something like this:
insert into my_table (field1, field2, ...)
values (row1_value1, row2_value2, ...),
(row2_value1, row2_value2, ...), ...
So if you create for example a StringBuilder, you could batch up 20, 50 or 100 inserts and issue much less insert statements.

Take a look at Performing Batch Operations Using DataAdapters (ADO.NET) if you want to do everything from your .NET app.
If you want the fastest possible performance you could use the bcp utility (assuming you're using SQL Server.) You would write your data to a delimited file and use bcp to perform the inserts.

What about writting all your insert statement in a string separated by ';' and pass this string to ExecuteNonQuery ?
to check for error just compare the number of insert with the row affected that ExecuteNonQuery return

How to speed up LINQ inserts with SQL CE?

History
I have a list of "records" (3,500) which I save to XML and compress on exit of the program. Since:
the number of the records increases
only around 50 records need to be updated on exit
saving takes about 3 seconds
I needed another solution -- embedded database. I chose SQL CE because it works with VS without any problems and the license is OK for me (I compared it to Firebird, SQLite, EffiProz, db4o and BerkeleyDB).
The data
The record structure: 11 fields, 2 of them make primary key (nvarchar + byte). Other records are bytes, datatimes, double and ints.
I don't use any relations, joins, indices (except for primary key), triggers, views, and so on. It is flat Dictionary actually -- pairs of Key+Value. I modify some of them, and then I have to update them in database. From time to time I add some new "records" and I need to store (insert) them. That's all.
LINQ approach
I have blank database (file), so I make 3500 inserts in a loop (one by one). I don't even check if the record already exists because db is blank.
Execution time? 4 minutes, 52 seconds. I fainted (mind you: XML + compress = 3 seconds).
SQL CE raw approach
I googled a bit, and despite such claims as here:
LINQ to SQL (CE) speed versus SqlCe
stating it is SQL CE itself fault I gave it a try.
The same loop but this time inserts are made with SqlCeResultSet (DirectTable mode, see: Bulk Insert In SQL Server CE) and SqlCeUpdatableRecord.
The outcome? Do you sit comfortably? Well... 0.3 second (yes, fraction of the second!).
The problem
LINQ is very readable, and raw operations are quite contrary. I could write a mapper which translates all column indexes to meaningful names, but it seems like reinventing the wheel -- after all it is already done in... LINQ.
So maybe it is a way to tell LINQ to speed things up? QUESTION -- how to do it?
The code
LINQ
foreach (var entry in dict.Entries.Where(it => it.AlteredByLearning))
{
PrimLibrary.Database.Progress record = null;
record = new PrimLibrary.Database.Progress();
record.Text = entry.Text;
record.Direction = (byte)entry.dir;
db.Progress.InsertOnSubmit(record);
record.Status = (byte)entry.LastLearningInfo.status.Value;
// ... and so on
db.SubmitChanges();
}
Raw operations
SqlCeCommand cmd = conn.CreateCommand();
cmd.CommandText = "Progress";
cmd.CommandType = System.Data.CommandType.TableDirect;
SqlCeResultSet rs = cmd.ExecuteResultSet(ResultSetOptions.Updatable);
foreach (var entry in dict.Entries.Where(it => it.AlteredByLearning))
{
SqlCeUpdatableRecord record = null;
record = rs.CreateRecord();
int col = 0;
record.SetString(col++, entry.Text);
record.SetByte(col++,(byte)entry.dir);
record.SetByte(col++,(byte)entry.LastLearningInfo.status.Value);
// ... and so on
rs.Insert(record);
}

Do more work per transaction.
Commits are generally very expensive operations for a typical relational database as the database must wait for disk flushes to ensure data is not lost (ACID guarantees and all that). Conventional HDD disk IO without specialty controllers is very slow in this sort of operation: the data must be flushed to the physical disk -- perhaps only 30-60 commits can occur a second with an IO sync between!
See the SQLite FAQ: INSERT is really slow - I can only do few dozen INSERTs per second. Ignoring the different database engine, this is the exact same issue.
Normally, LINQ2SQL creates a new implicit transaction inside SubmitChanges. To avoid this implicit transaction/commit (commits are expensive operations) either:
Call SubmitChanges less (say, once outside the loop) or;
Setup an explicit transaction scope (see TransactionScope).
One example of using a larger transaction context is:
using (var ts = new TransactionScope()) {
// LINQ2SQL will automatically enlist in the transaction scope.
// SubmitChanges now will NOT create a new transaction/commit each time.
DoImportStuffThatRunsWithinASingleTransaction();
// Important: Make sure to COMMIT the transaction.
// (The transaction used for SubmitChanges is committed to the DB.)
// This is when the disk sync actually has to happen,
// but it only happens once, not 3500 times!
ts.Complete();
}
However, the semantics of an approach using a single transaction or a single call to SubmitChanges are different than that of the code above calling SubmitChanges 3500 times and creating 3500 different implicit transactions. In particular, the size of the atomic operations (with respect to the database) is different and may not be suitable for all tasks.
For LINQ2SQL updates, changing the optimistic concurrency model (disabling it or just using a timestamp field, for instance) may result in small performance improvements. The biggest improvement, however, will come from reducing the number of commits that must be performed.
Happy coding.

i'm not positive on this, but it seems like the db.SubmitChanges() call should be made outside of the loop. maybe that would speed things up?