SQL Server数据库调用的多线程C＃应用程序

Question

I have a SQL Server database with 500,000 records in table main . There are also three other tables called child1 , child2 , and child3 . The many to many relationships between child1 , child2 , child3 , and main are implemented via the three relationship tables: main_child1_relationship , main_child2_relationship , and main_child3_relationship . I need to read the records in main , update main , and also insert into the relationship tables new rows as well as insert new records in the child tables. The records in the child tables have uniqueness constraints, so the pseudo-code for the actual calculation (CalculateDetails) would be something like:

for each record in main
{
   find its child1 like qualities
   for each one of its child1 qualities
   {
      find the record in child1 that matches that quality
      if found
      {
          add a record to main_child1_relationship to connect the two records
      }
      else
      {
          create a new record in child1 for the quality mentioned
          add a record to main_child1_relationship to connect the two records
      }
   }
   ...repeat the above for child2
   ...repeat the above for child3 
}

This works fine as a single threaded app. But it is too slow. The processing in C# is pretty heavy duty and takes too long. I want to turn this into a multi-threaded app.

What is the best way to do this? We are using Linq to Sql.

So far my approach has been to create a new DataContext object for each batch of records from main and use ThreadPool.QueueUserWorkItem to process it. However these batches are stepping on each other's toes because one thread adds a record and then the next thread tries to add the same one and ... I am getting all kinds of interesting SQL Server dead locks.

Here is the code:

    int skip = 0;
    List<int> thisBatch;
    Queue<List<int>> allBatches = new Queue<List<int>>();
    do
    {
        thisBatch = allIds
                .Skip(skip)
                .Take(numberOfRecordsToPullFromDBAtATime).ToList();
        allBatches.Enqueue(thisBatch);
        skip += numberOfRecordsToPullFromDBAtATime;

    } while (thisBatch.Count() > 0);

    while (allBatches.Count() > 0)
    {
        RRDataContext rrdc = new RRDataContext();

        var currentBatch = allBatches.Dequeue();
        lock (locker)  
        {
            runningTasks++;
        }
        System.Threading.ThreadPool.QueueUserWorkItem(x =>
                    ProcessBatch(currentBatch, rrdc));

        lock (locker) 
        {
            while (runningTasks > MAX_NUMBER_OF_THREADS)
            {
                 Monitor.Wait(locker);
                 UpdateGUI();
            }
        }
    }

And here is ProcessBatch:

    private static void ProcessBatch( 
        List<int> currentBatch, RRDataContext rrdc)
    {
        var topRecords = GetTopRecords(rrdc, currentBatch);
        CalculateDetails(rrdc, topRecords);
        rrdc.Dispose();

        lock (locker)
        {
            runningTasks--;
            Monitor.Pulse(locker);
        };
    }

And

    private static List<Record> GetTopRecords(RecipeRelationshipsDataContext rrdc, 
                                              List<int> thisBatch)
    {
        List<Record> topRecords;

        topRecords = rrdc.Records
                    .Where(x => thisBatch.Contains(x.Id))
                    .OrderBy(x => x.OrderByMe).ToList();
        return topRecords;
    }

CalculateDetails is best explained by the pseudo-code at the top.

I think there must be a better way to do this. Please help. Many thanks!

Answer 1

Here's my take on the problem:

• When using multiple threads to insert/update/query data in SQL Server, or any database, then deadlocks are a fact of life. You have to assume they will occur and handle them appropriately.

• That's not so say we shouldn't attempt to limit the occurence of deadlocks. However, it's easy to read up on the basic causes of deadlocks and take steps to prevent them, but SQL Server will always surprise you :-)

Some reason for deadlocks:

• Too many threads - try to limit the number of threads to a minimum, but of course we want more threads for maximum performance.

• Not enough indexes. If selects and updates aren't selective enough SQL will take out larger range locks than is healthy. Try to specify appropriate indexes.

• Too many indexes. Updating indexes causes deadlocks, so try to reduce indexes to the minimum required.

• Transaction isolational level too high. The default isolation level when using .NET is 'Serializable', whereas the default using SQL Server is 'Read Committed'. Reducing the isolation level can help a lot (if appropriate of course).

This is how I might tackle your problem:

• I wouldn't roll my own threading solution, I would use the TaskParallel library. My main method would look something like this:

   using (var dc = new TestDataContext()) { // Get all the ids of interest. // I assume you mark successfully updated rows in some way // in the update transaction. List<int> ids = dc.TestItems.Where(...).Select(item => item.Id).ToList(); var problematicIds = new List<ErrorType>(); // Either allow the TaskParallel library to select what it considers // as the optimum degree of parallelism by omitting the // ParallelOptions parameter, or specify what you want. Parallel.ForEach(ids, new ParallelOptions {MaxDegreeOfParallelism = 8}, id => CalculateDetails(id, problematicIds)); } 

• Execute the CalculateDetails method with retries for deadlock failures

   private static void CalculateDetails(int id, List<ErrorType> problematicIds) { try { // Handle deadlocks DeadlockRetryHelper.Execute(() => CalculateDetails(id)); } catch (Exception e) { // Too many deadlock retries (or other exception). // Record so we can diagnose problem or retry later problematicIds.Add(new ErrorType(id, e)); } } 

• The core CalculateDetails method

   private static void CalculateDetails(int id) { // Creating a new DeviceContext is not expensive. // No need to create outside of this method. using (var dc = new TestDataContext()) { // TODO: adjust IsolationLevel to minimize deadlocks // If you don't need to change the isolation level // then you can remove the TransactionScope altogether using (var scope = new TransactionScope( TransactionScopeOption.Required, new TransactionOptions {IsolationLevel = IsolationLevel.Serializable})) { TestItem item = dc.TestItems.Single(i => i.Id == id); // work done here dc.SubmitChanges(); scope.Complete(); } } } 

• And of course my implementation of a deadlock retry helper

   public static class DeadlockRetryHelper { private const int MaxRetries = 4; private const int SqlDeadlock = 1205; public static void Execute(Action action, int maxRetries = MaxRetries) { if (HasAmbientTransaction()) { // Deadlock blows out containing transaction // so no point retrying if already in tx. action(); } int retries = 0; while (retries < maxRetries) { try { action(); return; } catch (Exception e) { if (IsSqlDeadlock(e)) { retries++; // Delay subsequent retries - not sure if this helps or not Thread.Sleep(100 * retries); } else { throw; } } } action(); } private static bool HasAmbientTransaction() { return Transaction.Current != null; } private static bool IsSqlDeadlock(Exception exception) { if (exception == null) { return false; } var sqlException = exception as SqlException; if (sqlException != null && sqlException.Number == SqlDeadlock) { return true; } if (exception.InnerException != null) { return IsSqlDeadlock(exception.InnerException); } return false; } } 

• One further possibility is to use a partitioning strategy

If your tables can naturally be partitioned into several distinct sets of data, then you can either use SQL Server partitioned tables and indexes , or you could manually split your existing tables into several sets of tables. I would recommend using SQL Server's partitioning, since the second option would be messy. Also built-in partitioning is only available on SQL Enterprise Edition.

If partitioning is possible for you, you could choose a partion scheme that broke you data in lets say 8 distinct sets. Now you could use your original single threaded code, but have 8 threads each targetting a separate partition. Now there won't be any (or at least a minimum number of) deadlocks.

I hope that makes sense.

Answer 2

Overview

The root of your problem is that the L2S DataContext, like the Entity Framework's ObjectContext, is not thread-safe. As explained in this MSDN forum exchange , support for asynchronous operations in the .NET ORM solutions is still pending as of .NET 4.0; you'll have to roll your own solution, which as you've discovered isn't always easy to do when your framework assume single-threadedness.

I'll take this opportunity to note that L2S is built on top of ADO.NET, which itself fully supports asynchronous operation - personally, I would much prefer to deal directly with that lower layer and write the SQL myself, just to make sure that I fully understood what was transpiring over the network.

SQL Server Solution?

That being said, I have to ask - must this be a C# solution? If you can compose your solution out of a set of insert/update statements, you can just send over the SQL directly and your threading and performance problems vanish.* It seems to me that your problems are related not to the actual data transformations to be made, but center around making them performant from .NET. If .NET is removed from the equation, your task becomes simpler. After all, the best solution is often the one that has you writing the smallest amount of code, right? ;)

Even if your update/insert logic can't be expressed in a strictly set-relational manner, SQL Server does have a built-in mechanism for iterating over records and performing logic - while they are justly maligned for many use cases, cursors may in fact be appropriate for your task.

If this is a task that has to happen repeatedly, you could benefit greatly from coding it as a stored procedure.

*of course, long-running SQL brings its own problems like lock escalation and index usage that you'll have to contend with.

C# Solution

Of course, it may be that doing this in SQL is out of the question - maybe your code's decisions depend on data that comes from elsewhere, for example, or maybe your project has a strict 'no-SQL-allowed' convention. You mention some typical multithreading bugs, but without seeing your code I can't really be helpful with them specifically.

Doing this from C# is obviously viable, but you need to deal with the fact that a fixed amount of latency will exist for each and every call you make. You can mitigate the effects of network latency by using pooled connections, enabling multiple active result sets, and using the asynchronous Begin/End methods for executing your queries. Even with all of those, you will still have to accept that there is a cost to shipping data from SQL Server to your application.

One of the best ways to keep your code from stepping all over itself is to avoid sharing mutable data between threads as much as possible. That would mean not sharing the same DataContext across multiple threads. The next best approach is to lock critical sections of code that touch the shared data - lock blocks around all DataContext access, from the first read to the final write. That approach might just obviate the benefits of multithreading entirely; you can likely make your locking more fine-grained, but be ye warned that this is a path of pain.

Far better is to keep your operations separate from each other entirely. If you can partition your logic across 'main' records, that's ideal - that is to say, as long as there aren't relationships between the various child tables, and as long as one record in 'main' doesn't have implications for another, you can split your operations across multiple threads like this:

private IList<int> GetMainIds()
{
    using (var context = new MyDataContext())
        return context.Main.Select(m => m.Id).ToList();
}

private void FixUpSingleRecord(int mainRecordId)
{
    using (var localContext = new MyDataContext())
    {
        var main = localContext.Main.FirstOrDefault(m => m.Id == mainRecordId);

        if (main == null)
            return;

        foreach (var childOneQuality in main.ChildOneQualities)
        {
            // If child one is not found, create it
            // Create the relationship if needed
        }

        // Repeat for ChildTwo and ChildThree

        localContext.SaveChanges();
    }
}

public void FixUpMain()
{
    var ids = GetMainIds();
    foreach (var id in ids)
    {
        var localId = id; // Avoid closing over an iteration member
        ThreadPool.QueueUserWorkItem(delegate { FixUpSingleRecord(id) });
    }
}

Obviously this is as much a toy example as the pseudocode in your question, but hopefully it gets you thinking about how to scope your tasks such that there is no (or minimal) shared state between them. That, I think, will be the key to a correct C# solution.

EDIT Responding to updates and comments

If you're seeing data consistency issues, I'd advise enforcing transaction semantics - you can do this by using a System.Transactions.TransactionScope (add a reference to System.Transactions). Alternately, you might be able to do this on an ADO.NET level by accessing the inner connection and calling BeginTransaction on it (or whatever the DataConnection method is called).

You also mention deadlocks. That you're battling SQL Server deadlocks indicates that the actual SQL queries are stepping on each other's toes. Without knowing what is actually being sent over the wire, it's difficult to say in detail what's happening and how to fix it. Suffice to say that SQL deadlocks result from SQL queries, and not necessarily from C# threading constructs - you need to examine what exactly is going over the wire. My gut tells me that if each 'main' record is truly independent of the others, then there shouldn't be a need for row and table locks, and that Linq to SQL is likely the culprit here.

You can get a dump of the raw SQL emitted by L2S in your code by setting the DataContext.Log property to something eg Console.Out. Though I've never personally used it, I understand the LINQPad offers L2S facilities and you may be able to get at the SQL there, too.

SQL Server Management Studio will get you the rest of the way there - using the Activity Monitor, you can watch for lock escalation in real time. Using the Query Analyzer, you can get a view of exactly how SQL Server will execute your queries. With those, you should be able to get a good notion of what your code is doing server-side, and in turn how to go about fixing it.

Answer 3

I would recommend moving all the XML processing into the SQL server, too. Not only will all your deadlocks disappear, but you will see such a boost in performance that you will never want to go back.

It will be best explained by an example. In this example I assume that the XML blob already is going into your main table (I call it closet). I will assume the following schema:

CREATE TABLE closet (id int PRIMARY KEY, xmldoc ntext) 
CREATE TABLE shoe(id int PRIMARY KEY IDENTITY, color nvarchar(20))
CREATE TABLE closet_shoe_relationship (
    closet_id int REFERENCES closet(id),
    shoe_id int REFERENCES shoe(id)
)

And I expect that your data (main table only) initially looks like this:

INSERT INTO closet(id, xmldoc) VALUES (1, '<ROOT><shoe><color>blue</color></shoe></ROOT>')
INSERT INTO closet(id, xmldoc) VALUES (2, '<ROOT><shoe><color>red</color></shoe></ROOT>')

Then your whole task is as simple as the following:

INSERT INTO shoe(color) SELECT DISTINCT CAST(CAST(xmldoc AS xml).query('//shoe/color/text()') AS nvarchar) AS color from closet
INSERT INTO closet_shoe_relationship(closet_id, shoe_id) SELECT closet.id, shoe.id FROM shoe JOIN closet ON CAST(CAST(closet.xmldoc AS xml).query('//shoe/color/text()') AS nvarchar) = shoe.color

But given that you will do a lot of similar processing, you can make your life easier by declaring your main blob as XML type, and further simplifying to this:

INSERT INTO shoe(color)
    SELECT DISTINCT CAST(xmldoc.query('//shoe/color/text()') AS nvarchar)
    FROM closet
INSERT INTO closet_shoe_relationship(closet_id, shoe_id)
    SELECT closet.id, shoe.id
    FROM shoe JOIN closet
        ON CAST(xmldoc.query('//shoe/color/text()') AS nvarchar) = shoe.color

There are additional performance optimizations possible, like pre-computing repeatedly invoked Xpath results in a temporary or permanent table, or converting the initial population of the main table into a BULK INSERT, but I don't expect that you will really need those to succeed.

Answer 4

sql server deadlocks are normal & to be expected in this type of scenario - MS's recommendation is that these should be handled on the application side rather than the db side.

However if you do need to make sure that a stored procedure is only called once then you can use a sql mutex lock using sp_getapplock. Here's an example of how to implement this

BEGIN TRAN
DECLARE @mutex_result int;
EXEC @mutex_result = sp_getapplock @Resource = 'CheckSetFileTransferLock',
 @LockMode = 'Exclusive';

IF ( @mutex_result < 0)
BEGIN
    ROLLBACK TRAN

END

-- do some stuff

EXEC @mutex_result = sp_releaseapplock @Resource = 'CheckSetFileTransferLock'
COMMIT TRAN  

Answer 5

This may be obvious, but looping through each tuple and doing your work in your servlet container involves a lot of per-record overhead.

If possible, move some or all of that processing to the SQL server by rewriting your logic as one or more stored procedures.

Answer 6

If

• You don't have a lot of time to spend on this issue and need it to fix it right now
• You are sure that your code is done so that different thread will NOT modify the same record
• You are not afraid

Then ... you can just add "WITH NO LOCK" to your queries so that MSSQL doesn't apply the locks.

To use with caution :)

But anyway, you didn't tell us where the time is lost (in the mono-threaded version). Because if it's in the code, I'll advise you to write everything in the DB directly to avoid continuous data exchange. If it's in the db, I'll advise to check index (too much ?), i/o, cpu etc.