Phantom Read Problem in SQL Server

Phantom Read Concurrency Problem in SQL Server with Examples

In this article, I will discuss the Phantom Read Concurrency Problem in SQL Server with Examples. Please read our previous article discussing the Non-Repeatable Read Concurrency Problem in SQL Server with an example. At the end of this article, you will understand what the phantom read problem is and when it occurs in SQL Server, and you will also understand how to solve the phantom read problem in SQL Server.

What is the Phantom Read Concurrency Problem in SQL Server?

The Phantom Read concurrency problem in SQL Server occurs in a scenario where a transaction reads rows from a database table, and then, in the course of the same transaction, a subsequent read returns a set of rows that includes rows that were not visible in the first read. This generally happens when a second transaction inserts some new rows between the first and second query execution of the first transaction that matches the WHERE clause of the query executed by the first transaction. Essentially, the “phantoms” are the new rows appearing in the second read but not in the first read.

This problem is a concern in database systems because it can lead to inconsistent results within the same transaction, potentially causing incorrect data analysis or decision-making processes based on the read data.

Understanding Phantom Read Concurrency Problem in SQL Server

Let’s understand the Phantom Read Concurrency Problem in SQL Server with an example. We are going to use the following Employees table to understand this concept. 

Phantom Read Concurrency Problem in SQL Server with Examples

Please use the SQL script below to create and populate the Employees table with the required sample data.

-- Create Employee table
CREATE TABLE Employees
(
    Id INT PRIMARY KEY,
    Name VARCHAR(100),
    Gender VARCHAR(10)
)
Go

-- Insert some dummy data
INSERT INTO  Employees VALUES(1001,'Anurag', 'Male')
INSERT INTO  Employees VALUES(1002,'Priyanka', 'Female')
INSERT INTO  Employees VALUES(1003,'Pranaya', 'Male')
INSERT INTO  Employees VALUES(1004,'Hina', 'Female')

Let’s say we have two transactions, Transaction 1 and Transaction 2. Transaction 1 starts first, and it reads the data from the Employees table where Gender is Male, and it returns 2 rows for the first read, and then Transaction 1 does some other work. At this point, Transaction 2 started, inserting a new employee with Gender Male. Once Transaction 2 inserts the new employee, then Transaction 1 makes a second read, and it returns 3 rows, resulting in a Phantom Read Concurrency Problem in SQL Server. 

What is Phantom Read Problem in SQL Server?

Phantom Read Concurrency Problem in SQL Server with an Example: 

Let us understand the Phantom Read Concurrency Problem in SQL Server with an example. Open 2 instances of the SQL Server Management Studio. From the first instance, execute the Transaction 1 code, and from the second instance, execute the Transaction 2 code. Notice that when Transaction 1 is completed, it gets different rows for reading 1 and reading 2, resulting in a phantom read problem. The Read Committed, Read Uncommitted, and Repeatable Read Transaction Isolation Level causes Phantom Read Concurrency Problem in SQL Server. In the below Transactions, I am using REPEATABLE READ Transaction Isolation Level, and you can also use Read Committed and Read Uncommitted Transaction Isolation Levels.

Transaction 1
-- Transaction 1
SET TRANSACTION ISOLATION LEVEL REPEATABLE READ
BEGIN TRANSACTION
SELECT * FROM Employees where Gender = 'Male'
-- Do Some work
WAITFOR DELAY '00:00:10'
SELECT * FROM Employees where Gender = 'Male'
COMMIT TRANSACTION
Transaction 2
-- Transaction 2
SET TRANSACTION ISOLATION LEVEL REPEATABLE READ
BEGIN TRANSACTION
INSERT into Employees VALUES(1005, 'Sambit', 'Male')
COMMIT TRANSACTION
How Do We Solve the Phantom Read Concurrency Problem in SQL Server?

You can use the Serializable or Snapshot Transaction Isolation Level to solve the Phantom Read Concurrency Problem in SQL Server. In our example, to fix the Phantom Read Concurrency Problem, let’s set the transaction isolation level of Transaction 1 to serializable. The Serializable Transaction Isolation Level places a range lock on the rows returned by the transaction based on the condition. In our example, it will place a lock where Gender is Male, which prevents any other transaction from inserting new rows within that Gender. This solves the phantom read problem in SQL Server.  

When you execute Transactions 1 and 2 from 2 different instances of SQL Server Management Studio, Transaction 2 is blocked until Transaction 1 completes. At the end of Transaction 1, both the reads get the same number of rows. Modify the Transaction 1 code as follows:

-- Transaction 1
SET TRANSACTION ISOLATION LEVEL SERIALIZABLE
BEGIN TRANSACTION
SELECT * FROM Employees where Gender = 'Male'
-- Do Some work
WAITFOR DELAY '00:00:10'
SELECT * FROM Employees where Gender = 'Male'
COMMIT TRANSACTION

How Does SERIALIZABLE Transaction Isolation Level Work in SQL Server?

In SQL Server, the Serializable isolation level is the highest level of isolation that prevents non-repeatable reads, phantom reads, and other concurrency issues. Here is how the Serializable isolation level works to prevent non-repeatable reads, phantom reads, and other concurrency issues:

Shared Locks for Read Operations
  • Mechanism: At the Serializable isolation level, when a transaction reads data, the SQL Server places shared locks on the accessed data and keeps these locks until the transaction is completed. This prevents other transactions from modifying the data while it is locked.
  • Range Locks: To prevent phantom reads, SQL Server uses range locks in addition to shared locks. Range locks are placed on a range of keys in an index, preventing other transactions from inserting new rows into the range that a transaction reads.
Exclusive Locks for Write Operations
  • Mechanism: For write operations (such as INSERT, UPDATE, DELETE), the Serializable isolation level uses exclusive locks, which prevent other transactions from reading or writing the locked data. These locks are held until the transaction is completed.
  • Effectiveness: By ensuring that exclusive locks are held for the duration of the transaction, Serializable isolation guarantees that once a transaction modifies data, no other transactions can read or modify that data until the first transaction finishes.
How Does SERIALIZABLE Transaction Isolation Level Prevent Phantom Read Issue in SQL Server?

In SQL Server, the Serializable transaction isolation level prevents phantom read issues through the use of locks that ensure a transaction can repeat the same read operation with guaranteed consistent results, even if other transactions are executing concurrently. Phantom reads occur when a transaction reads the same set of rows more than once, and in between these reads, another transaction inserts new rows that fall within the range of what the first transaction is reading. This can lead to inconsistent data being read within the same transaction, a scenario the Serializable isolation level is designed to avoid. Here’s how it achieves this:

Range Locks

The primary mechanism by which the Serializable isolation level prevents phantom reads is through the use of range locks. Range locking is a technique where SQL Server locks a range of keys in an index, preventing other transactions from inserting, updating, or deleting rows within that range until the lock is released. This includes:

  • Key Range Locks: Locks that are placed on a range of keys within an index. They ensure that no other transaction can insert a new row that would fall within the range that the current transaction is reading.
How Range Locks Work?
  • Locking Strategy: When a transaction under the Serializable isolation level reads data using a range query (for example, a query with a WHERE clause that specifies a range of values), SQL Server automatically places range locks on the index entries that represent the range of values being queried. This prevents other transactions from inserting new rows into that range.
  • Shared and Exclusive Locks: In addition to range locks, Serializable also uses shared locks for reading data and exclusive locks for writing data, similar to lower isolation levels. However, the key difference at the Serializable level is the extensive use of range locks combined with these shared and exclusive locks to cover ranges of data.
  • Duration of Locks: These locks (shared, exclusive, and range) are held until the transaction is complete, not just for the duration of the individual read or write operation. This ensures the consistency of read operations throughout the transaction’s duration.

While the Serializable isolation level provides the highest level of data consistency by preventing phantom reads (as well as non-repeatable reads and dirty reads), it comes with trade-offs in terms of concurrency and performance. The extensive locking required can lead to increased blocking and reduced throughput as transactions wait for locks to be released. Therefore, it’s essential to consider the specific requirements of your application and the expected workload when choosing the appropriate isolation level.

In the next article, I will discuss the Snapshot Isolation Level in SQL Server with an example. In this article, I try to explain the Phantom Read Problem in SQL Server with examples. I would like to have your feedback. Please post your feedback, questions, or comments about this article.

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