![]() The application with the transaction that terminated with an error can retry the transaction, which usually completes after the other deadlocked transaction has finished.ĭeadlocking is often confused with normal blocking. This allows the other task to complete its transaction. If the monitor detects a cyclic dependency, it chooses one of the tasks as a victim and terminates its transaction with an error. the SQL Server Database Engine deadlock monitor periodically checks for tasks that are in a deadlock. This condition is also called a cyclic dependency: Transaction A has a dependency on transaction B, and transaction B closes the circle by having a dependency on transaction A.īoth transactions in a deadlock will wait forever unless the deadlock is broken by an external process. Transaction A cannot complete until transaction B completes, but transaction B is blocked by transaction A. Transaction B now requests an exclusive lock on row 1, and is blocked until transaction A finishes and releases the shared lock it has on row 1.Transaction A now requests an exclusive lock on row 2, and is blocked until transaction B finishes and releases the shared lock it has on row 2.Transaction B acquires a shared lock on row 2.Transaction A acquires a shared lock on row 1.Understand deadlocksĪ deadlock occurs when two or more tasks permanently block each other by each task having a lock on a resource that the other tasks are trying to lock. For more on transaction locking, see Transaction locking and row versioning guide.įor more specific information on identification and prevention of deadlocks in Azure SQL Database, see Analyze and prevent deadlocks in Azure SQL Database. Deadlocks are caused competing, concurrent locks in the database, often in multi-step transactions. This article discusses deadlocks in the SQL Server Database Engine in depth. These may well however be risks you are willing to take and/or mitigate against in order to improve concurrency.Applies to: SQL Server Azure SQL Database Azure SQL Managed Instance Azure Synapse Analytics Analytics Platform System (PDW) You'll find that less stringent isolation levels will allow the EF code to work, but at the risk of phantom records, non-repeatable reads etc. The Serializable isolation level can massively reduce concurrency and this example shows exactly why. The ExecuteSqlCommand only requires a single update statement and thus a deadlock does not occur. The other client itself then tries to obtain an exclusive lock and you have a deadlock scenario. Then when one or other of them first tries to perform the UPDATE they cannot get the requisite exclusive lock because the other client has a shared lock on it. With a serializable isolation level both client A and client B take a shared lock for the duration of the transaction on the record when the SELECT statement is run. and a subsequent UPDATE for the SaveChanges() call. ![]() This is due to the EF code generating two SQL statements: a SELECT for the line: var test = db.customer_table.Where(x => x.id = 38).FirstOrDefault() This is so confusing, what linq have done in behind making Transaction working inconsistent behavior? Var test = db.Database.ExecuteSqlCommand("Update customer_table set bank_holder_name = 'CLIENT XXXXX' where pu_id = 38") <= Client B is stop here and proceed after Client A is completedįinally, the transaction is working with code above (not linq function). I edited my code as below: using (myEntities db = new myEntities ()) Thus, I suspected this might caused by linq (incorrect row/ table lock) ![]() This is not what I expected where Client B should wait and not allowed to query any data about row id=38, but somehow it can proceed until SaveChanges and throws an error in the end. After CLIENT A finish commit, here will throw *Deadlock found error*" Test.bank_holder_name = "CLIENT NAME XXXX" ĭb.SaveChanges() <= CLIENT B stop here while client A still in progress. Var test = db.customer_table.Where(x => x.id = 38).FirstOrDefault() Client B needs to wait for Client A to commit his updatesīoth Client A & B instance are simulated and using this code: using (myEntities db = new myEntities ()).
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