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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# SQL query blocks server memory

I'm a bit newbie still and I have been assigned with the task of maintaining previosuly done code.
I have a web that simulates SQL Management Studio, limitating deleting options for example, so basic users don't screw our servers.
Well, we have a function that expects a query or queries, it works fine, but our server RAM gets blown up with complex queries, maybe it's not that much data, but its casting xml and all that stuff that I still don't even understand in SQL.
This is the actual function:
public DataSet ExecuteMultipleQueries(string queries)
{
var results = new DataSet();
using (var myConnection = new SqlConnection(_connectionString))
{
myConnection.Open();
var sqlCommand = myConnection.CreateCommand();
sqlCommand.Transaction = myConnection.BeginTransaction(IsolationLevel.ReadUncommitted);
sqlCommand.CommandTimeout = AppSettings.SqlTimeout;
sqlCommand.CommandText = queries.Trim();
var dataAdapter = new SqlDataAdapter { SelectCommand = sqlCommand };
dataAdapter.Fill(results);
return results;
}
}
I'm a bit lost, I've read many different answers but either I don't understand them properly or they don't solve my problems in any way.
I know I could use Linq-toSql- or Entity, I tried them but I really don't know how to use them with an "unknown" query, I could try to research more anyway so if you think they will help me approaching a solution, by any means, I will try to learn it.
So to the point:
The function seems to stop at dataAdapter.Fill(results) when debugging, at that point is where the server tries to answer the query and just consume all its RAM and blocks itself. How can I solve this? I thought maybe by making SQL return a certain amount of data, store it in a certain collection, then continue returning data, and keep going until there is no more data to return from SQL, but I really don't know how to detect if there is any data left to return from SQL.
Also I thought about reading and storing in two different threads, but I don't know how the data that is in one thread can be stored in other thread async (and even less if it solves the issue).
So, yes, I don't have anything clear at all, so any guidance or tip would be highly appreciated.
Thanks in advance and sorry for the long post.

You can use pagination to fetch only part of the data.
Your code will be like this:
dataAdapter.Fill(results, 0, pageSize);
pageSize can be at size you want (100 or 250 for example).
You can get more information in this msdn article.

In order to investigate, try the following:
Start SQL profiler (it is usually installed along with SSMS and can be started from Management Studio, Tools menu)
Make sure you fill up some filters (either NT username or at least the database you are profiling). This is to catch as specific (i.e. only your) queries as possible
Include starting events to see when your query starts (e.g. RPC:Starting).
Start your application
Start the profiler before issuing the query (fill the adapter)
Issue the query -> you should see the query start in the profiler
Stop the profiler not to catch other queries (it puts overhead on SQL Server)
Stop the application (no reason to mess with server until the analysis is done)
Take the query within SQL Management Studio. I expect a SELECT that returns a lot of data. Do not run as it is, but put a TOP to limit its results. E.g. SELECT TOP 1000 <some columns> from ....
If the TOPed select runs slowly, you are returning too much data.
This may be due to returning some large fields such as N/VARCHAR(MAX) or VARBINARY(MAX). One possible solution is to exclude these fields from the initial SELECT and lazy-load this data (as needed).
Check these steps and come back with your actual query, if needed.

Speed up model instantiation for EF6 / sqlite db

I have a large collection of 12000 data entries for example and want to insert them via EF6 into a sqlite database. The most time consumes the instantiation of the data models:
at the moment I loop 12000 times 'new myItem()'
downloaded12000Items.foreach(result =>{
var myItem= new myItem
{
Id = result.Id,
Description = result.Description,
Property1 = result.Property1
}
resultList.add(myItem);
});
unitOfWork.ItemRepository.InsertRange(resultList);
How can I speed up the instantiation of the models or is there maybe another way to insert the data faster into the sqlite database?
EDIT: I have to explain my problem better. The bottleneck is NOT the insert() into the database. To use EF6 .insert(someModel) you have to create an instance of a modelclass of your entity. I have to do this 12000 times, the instantiation of all the 12000 modelclasses takes too much time.
My question was, is there a possibility to fasten up the instatiation process of the model classes, maybe by cloning or something else?
Or, is there maybe a chance to insert the data into the sqlite db without using .insert(someModel), maybe by using a direct sql command or something else? Obviously skipping the model instantiation could be helpful...

The bottleneck is probably the adding of the entities to the context.
unitOfWork.ItemRepository.Insert(myItem);
At first it doesn't take much time, but after 100s or 1000 records, it does.
See also this answer for other optimizations you might be able to add (read the comments of the linked answer!).

How can I speed up the instantiation of the models or is there maybe another way to insert the data faster into the sqlite database?
Use the equivalent of await Context.SaveChangesAsync() in your repo after you have finished looping and inserting "12000 data entries" . Tell me more
Note it is no longer necessary to perform the following in order to improve performance:
context.Configuration.AutoDetectChangesEnabled = false; // out of date
context.Configuration.ValidateOnSaveEnabled = false; // out of date
...such code has its own drawbacks but more importantly it is based on out-of-date philosophy and does not take advantage of await in EF.
Here's a snippet of production code that I use to save an requirement realisation matrix:
// create your objects
var matrix = // in my prod code I create in excess of 32,600+ matrix cells
foreach (var cell in cellsToAdd)
{
matrix.Cells.Add(cell);
}
using (var context = new MyDbContext())
{
context.Matrices.Add (newMatrix);
await context.SaveChangesAsync();
}
I find this works perfectly well when I insert 32,646 matrix cells in my production environment. Simply using await and SaveChangesAsync() improved performance 12 times. Other strategies, like batching were not as effective and disabling options such as AutoDetectChangesEnabled though somewhat useful, arguably defeat the purpose of using an ORM.

Performance Tuning: Saving records exponentially grows every 500 records?

I wrote a class to read in a CSV file that houses 4k records, 72 col wide. The 'read()' into the List takes literally a second, maybe...
Once I have successfully loaded up the List, I have the general flow for saving each object to the db;
foreach (var object in Objects)
{
try{
// check conditions
// perform conversions on the data and assign to domain object.property
//....
//db.object.Add(object);
//db.savechanges();
}catch{
//update log if the try fails
}
}
Once the loop executes successfully, I call db.dispose();
I haven't built too many classes outside of the MVC controller structure because I'm new to this so go easy on me ;). I'm assuming that I'm tying up precious resources using this approach which is causing the exponential processing time issue.
Any suggestions on how to improve performance? Thanks in advance!

If you're working with SQL Server and willing to use something outside of Entity-framework there's a bulk copy routine that might be very useful for you. Basically what you do is to create a table in memory (its a .net object) and then add your records to that. Once you've added all 72K records to the table then you'll all at once save that to the database. Since this uses bulk copy functionality that's tuned for this scenario its extremely fast.
Here's a couple articles that might get you started:
http://www.codeproject.com/Articles/16922/SQL-Bulk-Copy-with-C-Net
http://www.codeproject.com/Articles/18418/Transferring-Data-Using-SqlBulkCopy
http://dotnetmentors.com/c-sharp/bulk-upload-into-sql-server-using-sqlbulkcopy-and-c-sharp.aspx

This credit should really go to Atoms for pointing out the "AutoDetectChangesEnabled" reference. I found a great article by Rick Strahl here; http://weblog.west-wind.com/posts/2013/Dec/22/Entity-Framework-and-slow-bulk-INSERTs which explains it well!
My 15 min processing just got knocked down to 45 seconds wooot!!!
Thanks!

Can I do a very large insert with Linq-to-SQL?

I've got some text data that I'm loading into a SQL Server 2005 database using Linq-to-SQL using this method (psuedo-code):
Create a DataContext
While (new data exists)
{
Read a record from the text file
Create a new Record
Populate the record
dataContext.InsertOnSubmit(record);
}
dataContext.SubmitChanges();
The code is a little C# console application. This works fine so far, but I'm about to do an import of the real data (rather than a test subset) and this contains about 2 million rows instead of the 1000 I've tested. Am I going to have to do some clever batching or something similar to avoid the code falling over or performing woefully, or should Linq-to-SQL handle this gracefully?

It looks like this would work however the changes (and thus memory) that are kept by the DataContext are going to grow with each InsertOnSubmit. Maybe it's adviseable to perform a SubmitChanges every 100 records?
I would also take a look at SqlBulkCopy to see if it doesn't fit your usecase better.

IF you need to do bulk inserts, you should check out SqlBulkCopy
Linq-to-SQL is not really suited for doing large-scale bulk inserts.

You would want to call SubmitChanges() every 1000 records or so to flush the changes so far otherwise you'll run out of memory.
If you want performance, you might want to bypass Linq-To-SQL and go for System.Data.SqlClient.SqlBulkCopy instead.

Just for the record I did as marc_s and Peter suggested and chunked the data. It's not especially fast (it took about an hour and a half as Debug configuration, with the debugger attached and quite a lot of console progress output), but it's perfectly adequate for our needs:
Create a DataContext
numRows = 0;
While (new data exists)
{
Read a record from the text file
Create a new Record
Populate the record
dataContext.InsertOnSubmit(record)
// Submit the changes in thousand row batches
if (numRows % 1000 == 999)
dataContext.SubmitChanges()
numRows++
}
dataContext.SubmitChanges()