We Keep Coding

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#: Connection Errors opening SqlConnection inside Parallel.ForEach

I have a .NET Core C# console application that performs a large number of calculations and then writes the results to a SQL Server 2016 Developer edition database using Dapper (and Dapper.Contrib). The issue I'm having is that when I run a lot of items in parallel (greater than 1000, for example), I start getting intermittent connection failures on the .Open() call, saying
A network-related or instance-specific error occurred...
This often happens after several thousand rows have already been inserted successfully.
A simplified version of the code would look like the following:
Parallel.ForEach(collection, (item) =>
{
var results = item.Calculate(parameters);
dal.Results.Insert(results);
allResults.AddRange(results);
});
And inside the Insert method, it looks like this:
public override void Insert(IEnumerable<Result> entities)
{
using (var connection = GetConnection())
{
connection.Open();
using (var transaction = connection.BeginTransaction(IsolationLevel.ReadCommitted))
{
connection.Insert(entities, transaction);
transaction.Commit();
}
}
}
Some other things about the code that I don't think are affecting this but might be relevant:
dal.Results is simply a repository that contains that Insert() method and is preinitialized with a connection string that is used to instantiate a new SqlConnection(connectionString) every time GetConnection() is called.
allResults is a ConcurrentBag<Result> that I'm using to store all the results for later use outside the Parallel.ForEach
I'm using a transaction because it seems to perform better this way, but I'm open to suggestions if that could be causing problems.
Thanks in advance for any guidance on this issue!

There is no advantage to execute heavily IO-bound db-operations in parallel.
You should create fever but bigger bunches of data to be inserted with minimun amount of database transactions. That can be achieve with several ways:
With SQL Bulk Insert operations provided by .NET framework
By using external library which is specialized on high-speed Bulk operations
By crafting sql stored procedure which takes array of data as parameter. More information about table-valued parameters can be found in https://learn.microsoft.com/en-us/sql/relational-databases/tables/use-table-valued-parameters-database-engine
So try following: Execute CPU-intensive calculations in parallel loop and save allResults into database after loop.

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

using SQL server to store data

I'm right now using SQL server 2008 in my project to store and fetch data . this is going perfect till now . I can fetch 20000 records in less than 50ms (JSON) . but facing a problem with inserts stuff . in my project I need to be able to insert something like 100000 records every minute . and this is seems to be very slow with SQL server .
I've tried to use another database (NOSQL DB) like mongoDB which are very fast in storing data (5s) comparing to SQLServer(270s) but not fast as sql in fetching data(20000 => 180ms) .
So I'm asking here if there any way to make SQL faster in storing . or to make mongoDB faster in fetching ( I'm not an expert in mongoDB I know the very basic things about it ) .
public static void ExecuteNonQuery(string sql)
{
SqlConnection con = GetConnection();
con.Open();
SqlCommand cmd = new SqlCommand(sql, con);
try
{
cmd.ExecuteNonQuery();
}
finally
{
con.Close();
}
}
SQL's Insert function
public IEnumerable<T> GetRecords<T>(System.Linq.Expressions.Expression<Func<T, bool>> expression, int from, int to) where T : class, new()
{
return _db.GetCollection<T>(collectionName).Find<T>(expression).Skip(from).Limit(to).Documents;
}
Mongo's Select function ( MongoDB 1.6 )
Update
: data structure : (int) Id , (string) Data

I guess that you are executing each insert in a transaction of its own (an implicit transaction might have been created if you do not provide one explicitly). As SQL server needs to ensure that the transaction is committed to the hard drive each transaction has a overhead that is very significant.
To get things to go faster, try to perform many inserts (try with a thousand or so) in a single ExecuteNonQuery() call. Also do not open and close, but keep the connection open (thus being in the same transaction) for several inserts.

You should have a look at the SqlBulkCopy Class
http://msdn.microsoft.com/en-us/library/system.data.sqlclient.sqlbulkcopy.aspx

MongoDB is very fast on reads and writes. 50k reads and writes per second is doable on commodity hardware - depending on the data size. In addition to that you always have the option to scale out with sharding and replica sets but as said: 20k operations per seconds
with MongoDB is nothing.

Generally the speed on inserting data into the database is a function on the complexity of the operation.
If your inserts are significantly slow, then it points to optimisation problems with the inserts. Identify exaxtly what SQL insert statements your program is generating and then use the database EXPLAIN function to figure out what operations the underlying database is using. This often gives you a clue as to how you need to change your setup to increase the speed of these operations.
It might mean you have to change your database, or it might mean batching your inserts into a single call rather than inserting each item separately.
I see you are setting up and closing the connection each time.. this takes a significant time in itself. Try using a persistent connection.

How to manage connection and command object with multiple threads

I'm a novice C# dev and I'm writing a database app the performs updates on two different tables and inserts on another two tables and each process is running on it's own separate thread. So I have two threads handling inserts on two different tables and two threads handling updates on two different tables. Each process is updating and inserting approximately 4 or 5 times per second so I don't close the connection until the complete session is over then I close the entire app. I wanted to know if I should be closing the connection after each insert and update even though I'm preforming these operations so frequently. 2nd, should I have each thread running on it's own connection and command object.
By the way I'm writing the app in C# and the database is MySQL. Also, as of now I'm using one connection and command object for all four threads. I keep getting an error message saying "There is already an open DataReader associated with this connection that must be closed first", that's why I'm asking if I should be using multiple connection and command objects.
Thanks
-Donld

If you enable connection pooling, it should enable optimal use of MySql connections for your scenario. Either way, generally the best pattern to follow is:
Acquire and open connection
Do work
Close/release connection
Something similar to (I'm a bit rusty on the class names for the MySql connector, so this may not be exactly correct, but you should get the general idea!):
private void DoMyPieceOfWork(int value1, int value2)
{
using(MySqlConnection connection = new MySqlConnection(
CONNECTION_STRING_GOES_HERE))
{
connection.Open();
using(MySqlCommand command = new MySqlCommand(
"INSERT INTO TABLE `blah` (Column1, Column2) VALUES #column1, #column2"))
{
command.Parameters.Add("#column1", MySqlType.Int).Value = value1;
command.Parameters.Add("#column2", MySqlType.Int).Value = value2;
command.ExecuteNonQuery();
}
connection.Close();
}
}
Of course this is a contrived, simplistic, example but the gist of it stands.

you have either to create a new connection for each thread, or (it's an idea) create a synchronized queue of command. Then process the queue in a single working thread.
You may also take a look as the Task class of the .Net framework 4