Database Transaction Isolation Levels: Why They Are Always Critical?

The isolation levels of database transactions are a topic that beginners in software development often see as a "detail," but as I gained experience, I understood how critical it is. Over the years, I've repeatedly experienced how this seemingly simple setting deeply impacts everything from an application's data integrity and performance to the accuracy of business processes. This post will explain why, in my opinion, it's so important and at which points it has come up in my career.

While working on a production ERP system, we were experiencing inconsistencies with shipment lists. When the report was pulled a few times during the day, different totals were seen, causing significant confusion. At first, I searched for a bug in the application layer, but the root cause was actually stemming from the database's default transaction isolation level and other concurrently running transactions. Such subtle details often don't come to mind, but they can be key to solving underlying problems at the heart of the system.

Database Transaction Isolation Levels: A Fundamental Overview

Transaction Isolation Levels determine how much concurrent transactions in a database will affect each other. The ANSI SQL standard defines four main levels: Read Uncommitted, Read Committed, Repeatable Read, and Serializable. However, each database system implements these levels differently based on its internal architecture. For example, PostgreSQL does not support the Read Uncommitted level, and Read Committed is the default.

In my experience, these levels are not just theoretical definitions but practical decisions that directly affect the reliability and data integrity of an application. A wrong choice can lead to serious consequences such as inconsistencies in financial reports, stock errors, or corruption of customer data. Therefore, understanding and using these levels correctly when designing a system or debugging an existing one is, in my opinion, a must-have.

ℹ️ Why is it Important?

Database transaction isolation levels control how much concurrent transactions will affect each other. This is critically important for data consistency and the accuracy of business workflows, especially in systems with high concurrency. Proceeding without understanding the default level can invite insidious errors that are difficult to resolve later.

In a production ERP, an operator pulling daily production reports seeing different numbers in the morning versus the afternoon shook operational confidence. The reason was simple: the reporting process was running at the READ COMMITTED level, while new production records were being added and some were being updated during the day. Since the report saw the latest committed data with each SELECT query, it produced inconsistent results due to the changing data during the transaction's duration. This was a perfect example of "Phantom Read" and "Non-Repeatable Read" scenarios and was the first signal for me that I needed to research this topic more deeply.

READ COMMITTED and Its Unexpected Outcomes

READ COMMITTED is the default isolation level for many databases (including PostgreSQL) and is generally considered sufficient for most applications. At this level, a transaction only sees data that has been committed by other transactions. This prevents "dirty reads," meaning you don't read data that is uncommitted and potentially will be rolled back. However, this level is susceptible to "non-repeatable reads" and "phantom reads."

In my experience, the flexibility offered by READ COMMITTED has sometimes led to insidious problems. Especially when dealing with long-running transactions or reports involving multiple SELECT queries, I've seen data I read at the beginning of a transaction change by the time the transaction progresses, because another transaction updated and committed it. This situation can create critical inconsistencies, particularly in areas like data analysis or financial reporting.

We had a transaction in the inventory management module of an e-commerce platform that checked the stock status of a product and then made a reservation. The transaction would query the current stock of the product, then reserve a certain quantity for the customer, and then update the stock quantity. If another transaction changed the stock of the same product between the first query and the update, the transaction running at the READ COMMITTED level could make a reservation with an incorrect stock quantity. This carried the risk of telling a customer "the product is available" and then later saying "it's out of stock."

BEGIN;
-- First SELECT: stock 100
SELECT stock_quantity FROM products WHERE id = 123;

-- Meanwhile, another transaction reduces the stock to 90 and commits.

-- Second SELECT: stock 90
SELECT stock_quantity FROM products WHERE id = 123;
COMMIT;

The scenario above illustrates how inconsistent reads can occur even within the same transaction under READ COMMITTED. If the business logic is sensitive to such fluctuations, it might be necessary to move to a higher isolation level or use additional locking mechanisms at the application layer. For me, this showed that not only database settings but also the design of the business workflow and the application are directly related to these levels.

REPEATABLE READ: A Step Towards Consistency

The REPEATABLE READ isolation level is designed to address the non-repeatable read problem that READ COMMITTED cannot solve. At this level, all data read by a transaction remains the same until that transaction completes. This means that no matter how many times you run the same SELECT query within the same transaction, you will always get the same result set. This is very important for ensuring data consistency, especially in reporting and analytical operations.

However, even REPEATABLE READ does not always provide full protection against "phantom reads." Phantom reads occur when the set of rows returned by a query changes during a transaction; that is, if there were 5 rows satisfying a certain condition at the start of a transaction, and midway through the transaction another transaction adds a new row that satisfies the same condition, the same query might see this new row. PostgreSQL's REPEATABLE READ implementation actually prevents phantom reads (thanks to its MVCC snapshot), but the situation might differ in other database systems. Therefore, it's crucial to distinguish between standard definitions and specific database behaviors.

In the financial calculators of a side project, a task management application, I was generating complex reports. For these reports to be accurate, all financial records read during the report period needed to remain constant. When I ran the report with READ COMMITTED, my calculations were constantly changing because other transactions were adding new income/expense records intermittently. I resolved this issue by switching to REPEATABLE READ.

SET TRANSACTION ISOLATION LEVEL REPEATABLE READ;
BEGIN;
-- First SELECT: Get all income items
SELECT SUM(amount) FROM transactions WHERE type = 'income' AND date BETWEEN '2026-01-01' AND '2026-01-31';

-- Meanwhile, another transaction adds a new income record for the same dates and commits.

-- Second SELECT: Run the same query again, the result should be equal to the first
SELECT SUM(amount) FROM transactions WHERE type = 'income' AND date BETWEEN '2026-01-01' AND '2026-01-31';
COMMIT;

In this example, thanks to REPEATABLE READ, the second SELECT query will return the same result by using the snapshot of the data seen by the first query. This provides great reliability for applications that need to analyze data at a specific point in time or for reporting. However, it's important not to forget that this level comes with increased resource usage and potential locking risks. Although PostgreSQL's MVCC (Multi-Version Concurrency Control) structure minimizes these risks, being prepared for transaction retries is always good.

SERIALIZABLE: The Balance of Security and Performance

SERIALIZABLE is the highest isolation level in the ANSI SQL standard. At this level, all concurrently running transactions appear to have executed one after another, in sequence (serially). This completely prevents a transaction from seeing any inconsistency created by another transaction; meaning, all anomalies like dirty reads, non-repeatable reads, and phantom reads are eliminated. It is an ideal choice for critical applications where data integrity is of absolute priority.

However, this level of security comes at a cost: performance. SERIALIZABLE transactions can encounter serialization_failure errors when conflicting transactions occur and must be rolled back. This means the application needs to catch these errors and retry the transaction. This extra overhead can create a significant bottleneck, especially in systems with high concurrency and intensive write operations.

While designing a complex reconciliation process for a bank's internal platform, I had to use SERIALIZABLE. This process involved money transfers and balance updates between different accounts, and even the slightest inconsistency was unacceptable. Initially, we experienced frequent transaction interruptions because we hadn't adequately handled serialization_failure errors. The debugging process taught us that we needed to catch these errors and automatically retry the transactions.

import psycopg2
from psycopg2 import errors

def run_serializable_transaction(conn_string, query_func):
    for _ in range(5): # Give 5 retry attempts
        conn = None
        try:
            conn = psycopg2.connect(conn_string)
            conn.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_SERIALIZABLE)
            cur = conn.cursor()
            query_func(cur)
            conn.commit()
            return True
        except errors.SerializationFailure:
            print("SerializationFailure detected, retrying...")
            if conn:
                conn.rollback() # Roll back the transaction
        except Exception as e:
            print(f"An unexpected error occurred: {e}")
            if conn:
                conn.rollback()
            return False
        finally:
            if conn:
                conn.close()
    print("Max retries reached, transaction failed.")
    return False

# Example usage
def my_complex_query(cursor):
    cursor.execute("UPDATE accounts SET balance = balance - 100 WHERE id = 1;")
    cursor.execute("UPDATE accounts SET balance = balance + 100 WHERE id = 2;")

# run_serializable_transaction("dbname=mydb user=myuser", my_complex_query)

This example shows how to catch and retry SerializationFailure errors when performing a SERIALIZABLE level transaction. This simple retry mechanism significantly increased the application's resilience and reliability. When using SERIALIZABLE, it's important to remember that the application layer must be prepared for such errors, which was a big lesson for me. To minimize performance losses, limiting this level to truly critical and few transactions is the wisest approach.

Trade-offs and Misconceptions in Real-World Applications

Choosing database isolation levels is always a matter of trade-offs: you have to strike a balance between data consistency, concurrency, and performance. The idea of "always use the highest level because it's the safest" often leads to unsustainable performance issues in practice. On the other hand, the notion that "the default level is always sufficient" can pave the way for insidious data corruption.

In a client project, I worked with a team that insisted on using SERIALIZABLE in a system with heavy data entry. As a result, we were experiencing up to 15% transaction failure rates per day due to database deadlocks and serialization_failure errors. After detailed analysis and profiling, we demonstrated that most transactions could be sufficiently secured with READ COMMITTED or specific parts with REPEATABLE READ. After selecting the correct levels, the transaction failure rate dropped below 0.5%.

This situation showed that choosing the isolation level is not just a technical decision but must also reflect the nature of business processes and the application's expectations. Business logic dictates how long certain data needs to remain consistent. For instance, while an instant change in the quantity of a product in a user's cart might be acceptable, it's unacceptable for a financial report to yield different results when run twice.

⚠️ Misconception: Always the Highest Level

Some developers believe that always using SERIALIZABLE is the best way to guarantee data integrity. However, this can lead to significant negative impacts on concurrency and performance. SERIALIZABLE, especially in high-concurrency write operations, can cause serialization_failure errors, requiring the application to handle retry mechanisms. In my experience, this level has only been suitable for the most critical and few transactions. Choosing the right level depends on a good understanding of the application's business requirements and performance expectations.

Making the right decision regarding database isolation levels is also an integral part of database performance optimization. Using an unnecessarily high isolation level can increase locking contention, reducing the system's overall throughput. Therefore, we should always choose the least restrictive level, but ensure that this level meets the business requirements. In my previous post, [related: my experiences in database performance optimization], I discussed these balances in more detail.

The Place of Isolation Levels in My Career: Why is This Important Knowledge?

There's a specific reason I'm writing about this topic in the career category: a deep understanding of database transaction isolation levels, in my opinion, is a critical differentiator that directly indicates a software developer's or system architect's competency level. You can develop many applications with superficial knowledge, but when data integrity issues arise, knowing these details sets you apart.

In my career, this knowledge has enabled me to be a "hero" on multiple occasions. In a client project, there were minor inconsistencies that appeared periodically in financial reports, which had been unresolved for months. Everyone blamed the reporting tool, the business logic, or manual data entry errors. However, my knowledge of transaction isolation levels allowed me to realize that a complex multi-step transaction, running at the READ COMMITTED level, was being affected by another concurrent transaction changing the data, causing the report to take a different snapshot midway. This was resolved with a simple SET TRANSACTION ISOLATION LEVEL command, and I earned the team's trust.

This kind of understanding not only helps in fixing bugs but also in designing more robust and scalable systems. When making system architecture decisions, transitioning from a monolith to microservices, or implementing architectural patterns like event-sourcing, knowing how transactions are isolated is critical. In my post [related: my experiences in enterprise software architecture decisions], I explained the motivations behind such architectural choices in more detail.

This knowledge allows you to foresee potential future problems and make the right design choices from the outset. It's not enough for a system to just "work"; it must also "work correctly." Database transaction isolation levels are one of the cornerstones of this "working correctly."

Conclusion: Isolation Levels Are Not a Luxury, They Are a Necessity

Database transaction isolation levels are a critical topic, often overlooked, yet they form the foundation of data integrity and system reliability. In my nearly 20 years of experience, I've repeatedly seen that understanding this topic is more than just a technical detail; it's the key to building robust and reliable software systems. While we live in a world where READ COMMITTED is the default, knowing when your business requirements necessitate REPEATABLE READ or SERIALIZABLE protects you from potential data disasters.

Investing time in this topic is essential not only to become a good database administrator or developer but also to become a professional who can identify and resolve insidious errors in complex systems, thus making a difference in their career. My clear position is: understanding what these levels mean, what problems they cause in which scenarios, and which level brings which trade-offs, is a fundamental competency that every technology professional should have in their knowledge base.