Understanding PostgreSQL’s Concurrency Control: Isolation Levels Made Simple

Introduction

In our previous blog,we explored how Write-Ahead Logging (WAL) ensures durability and enables recovery after crashes. However, keeping data safe is only one piece of the puzzle in a multi-user database system. Equally important is how PostgreSQL manages concurrent transactions to maintain consistency and prevent conflicts.

A fundamental challenge in database systems is handling concurrent transactions without sacrificing data consistency. PostgreSQL addresses this with MVCC (Multiversion Concurrency Control), which allows transactions to work with snapshots of data, and with isolation levels, which define how visible the effects of one transaction are to others.

In PostgreSQL, three isolation levels are supported:

• Read Committed
  
• Repeatable Read
  
• Serializable
  

Although the SQL standard also includes Read Uncommitted, PostgreSQL treats it the same as Read Committed, so it is not offered separately.

Each of these levels provides a different trade-off between consistency, concurrency, and performance. For example, Read Committed is sufficient for many transactional systems where fresh data is preferred, while Serializable ensures strict correctness for critical financial or auditing workloads.

In this blog, we will explore these three isolation levels in depth and show practical PostgreSQL examples to understand their behavior in real-world scenarios.

Transaction Isolation Levels Overview

The SQL standard defines four isolation levels, PostgreSQL implements three distinct levels (treating Read Uncommitted as Read Committed)

Isolation Level	Dirty Read	Nonrepeatable Read	Phantom Read	Serialization Anomaly
Read Uncommitted	Not implemented	Possible	Possible	Possible
Read Committed	No	Possible	Possible	Possible
Repeatable Read	No	No	No	Possible
Serializable	No	No	No	No

1. Read Committed Isolation Level

The default isolation level in PostgreSQL.A statement within a transaction sees only data committed before it began.Each statement can see different committed data, even within the same transaction.

Key Properties:

• Prevents dirty reads.
• Allows non-repeatable reads and phantom reads.

Example:

-- Session 1:
BEGIN;
SELECT stock FROM products WHERE id = 1; -- returns 10

-- Session 2:
BEGIN;
UPDATE products SET stock = 5000 WHERE id = 1;
COMMIT;

-- Session 1:
SELECT stock FROM products WHERE id = 1; -- returns 5000
COMMIT;

Each query within Session 1 sees the most recently committed data at query time, so the second SELECT reflects Session 2’s update.

Real-World Analogy:

Imagine you open your bank app to check your account balance.

• Your friend has started transferring ₹1,000 to your account but hasn’t pressed “Confirm” yet.
  
• Since the transaction is still in progress, your app doesn’t reflect the incoming money.
  
• You only see the last committed (confirmed) balance.
  

You can’t see half-done transactions — only finalized ones.

2. Repeatable Read Isolation Level

Its Stronger than Read Committed.All queries within a transaction see a snapshot of the database taken when the transaction started.This ensures repeatable reads, multiple queries on the same data return consistent results within one transaction.

Key Properties:

• Prevents non-repeatable reads (the same query returns the same result within a transaction).
• Stronger consistency compared to Read Committed.

Example:

-- Session 1:
BEGIN TRANSACTION ISOLATION LEVEL REPEATABLE READ;
SELECT balance FROM accounts WHERE id = 123; -- returns 100

-- Session 2:
BEGIN;
UPDATE accounts SET balance = 200 WHERE id = 123;
COMMIT;

-- Session 1:
SELECT balance FROM accounts WHERE id = 123; -- still returns 100

-- When trying to update:
UPDATE accounts SET balance = 150 WHERE id = 123;
COMMIT; -- may throw serialization error

Session 1 sees a fixed snapshot and conflicts with Session 2’s committed update, which may cause Session 1 to abort and retry.

Real-World Analogy:

Imagine you’re filling out a loan application.

• You log in and check your salary balance as proof of funds. It shows ₹50,000.
  
• While you’re still filling out the form, your salary gets credited, and your balance is now ₹60,000.
  
• But since you started the loan application transaction earlier, the system keeps showing you the original ₹50,000 until you finish.
  

Your view remains consistent during your transaction, even if reality changes.

3. Serializable Isolation Level

The strictest isolation level in PostgreSQL.Ensures transactions behave as though they were executed sequentially, one after the other.Prevents dirty reads, non-repeatable reads, and phantom reads. Uses Serializable Snapshot Isolation (SSI) in PostgreSQL to detect and roll back unsafe transactions.

Key Properties:

• Guarantees full consistency.
• May result in transaction rollbacks if conflicts are detected.

Example:

-- Session 1
BEGIN ISOLATION LEVEL SERIALIZABLE;
SELECT SUM(balance) FROM accounts; -- Suppose result = 10000

-- Session 2
BEGIN ISOLATION LEVEL SERIALIZABLE;
UPDATE accounts SET balance = balance - 200 WHERE id = 1;
COMMIT;

-- Session 1
UPDATE accounts SET balance = balance + 200 WHERE id = 2;
-- ERROR: could not serialize access due to concurrent update
ROLLBACK;

Here, PostgreSQL detects a conflict between Session 1 and Session 2 and rolls back one transaction to preserve serializable behavior.

Real-World Analogy:

Imagine you and your friend are both trying to book the last seat on a train.

• If the system allowed both of you to try at the same time, it could cause chaos.
  
• In Serializable mode, the system makes one of you wait until the other finishes.
  
• Only one confirmed booking is possible, and the other transaction is rolled back or retried.
  

Ensures no conflicts by handling transactions one at a time (like a queue).

Choosing the Right Isolation Level

Isolation Level	Dirty Read	Nonrepeatable Read	Phantom Read	Serialization Anomaly	Performance Impact
Read Committed	No	Yes	Yes	Yes	Default, allows some anomalies
Repeatable Read	No	No	No	Yes	Fixed snapshot, requires retries
Serializable	No	No	No	No	Strictest, requires handling failures

Conclusion

PostgreSQL offers flexible isolation levels that allow developers and DBAs to strike the right balance between consistency and performance.

• Use Read Committed for general workloads where performance is key.
  
• Use Repeatable Read when you need consistent snapshots for reporting or analytics.
  
• Use Serializable for financial, auditing, or mission-critical workloads requiring strict correctness.
  

By understanding and applying the correct isolation level, you can prevent data anomalies while ensuring smooth concurrent access in PostgreSQL applications.