DBMS - Phantom Read Problem

Phantom read problems are common issue in database transactions, particularly when the transaction isolation level is set to "Read Committed". It happens when a new row is added or more than one rows are added to a dataset by another transaction while the initial transaction is still in progress. It may create inconsistencies, as the first transaction might see different data sets at different times. Read this chapter to learn the Phantom Read Problem in detail.

The Phantom Read Problem

The phantom read problem occurs when one transaction reads a set of records and thereafter finds that new records matching its search criteria have appeared due to another transaction. This can cause unexpected behavior and data inconsistencies.

The issue arises in databases when the isolation level "Read Committed" allows a transaction to read only committed data. If another transaction inserts new rows after the first transaction, then it has started but before it completes. Now the first transaction might "see" new rows when it reads the data again. The problem is quite confusing and we must handle them properly to avoid inconsistency. The phantom reads create a significant challenge in database consistency. If left unchecked, they can lead to incorrect calculations, inaccurate reports, and faulty decisions based on incomplete or outdated information.

Practical Example: Social Media Posts

To understand the phantom read problem better, let us understand through a real life example. Consider the social media website with the following database schema −

• Users Table − Contains user details.
• Post Table − Stores posts made by users.
• User Stats Table − Tracks how many posts each user has made.

Let us say a user publishes a post. Now the system needs to perform three actions in a single transaction −

• Insert the Post − Add a new entry to the Post table.
• Update User Stats − Increase the post count in the User Stats table.
• Fetch All Posts − Retrieve all posts made by the user.

Example in Action − Consider the following tables –

Users Table

Posts Table

Users Stats

Consider two transactions running simultaneously −

Transaction 1 (T1)

• Inserts a new post (Post ID 4, User ID 1).
• Updates the user's total post count to 4.
• Retrieves all posts for User ID 1.

Transaction 2 (T2)

• Starts after T1 inserts the post but before T1 completes.
• Inserts another post (Post ID 5, User ID 1).
• Commits immediately.

When T1 fetches all posts for the user at this time, then it sees five posts. This is because T2 has already committed its insert. However, the User Stats table only records four posts, as T1 updated it before knowing about the new post from T2. It happens because the isolation level is called "Read Committed". Here the T1 reads only committed data. When T2 commits its new post before T1 reads the data. So, the T1 gets an unexpected extra row. This is the classic phantom read problem.

For large-scale database applications like banking or e-commerce, the phantom reads can cause account balance mismatches, incorrect order totals, and inventory miscalculations. These problems may leading to significant operational issues.

Solutions for the Phantom Read Problem

Database designers and administrators may consider the following solutions to solve the Phantom Read Problem –

Use a Higher Isolation Level

To solve the phantom read problem, the simplest way is to change the isolation level to Repeatable Read or Serializable. These steps can prevent phantom reads by locking the relevant data sets.

• Repeatable Read − It prevents changes to existing rows but still allows new rows.
• Serializable − It prevents any other transaction from inserting, updating, or deleting relevant rows until the first transaction completes.

Apply Locking Mechanisms

Consider using the method of explicit locking mechanisms.

• Shared Locks − This prevent data modifications while reading.
• Exclusive Locks − This prevent other transactions from reading or writing until the lock is released.

Consider Row-Level Locks

Some databases support row-level locking. Here only specific rows are locked rather than the entire table. Now this can reduce contention while still preventing phantom reads.

• Database-Specific Solutions − Depending on the database system (like MySQL, PostgreSQL), the specific locking strategies can be used. Always consult the database’s official documentation to choose the best solution.
• Application-Level Handling − In some cases, the developers can implement application-level checks. This helps to detect and correct inconsistencies. While this is not ideal, it can serve as a last option as a solution.

Phantom Reads occur when new rows are added by another transaction during an ongoing transaction. Read Committed isolation level helps in this issue because it only reads committed data. We can use Repeatable Read or Serializable isolation levels to prevent phantom reads.

Conclusion

In this chapter, we explained what the phantom read problem is, why it happens, and how it creates inconsistencies in database transactions. We also explored a practical example involving a social media app and discussed ways to fix the issue using isolation levels and locking mechanisms.