Data Consistency, Anomalies, and the Role of Isolation - Chapter 3
π Isolation Levels and Handling Anomalies
So far, weβve explored various types of anomalies and examined them in depth through practical examples.
In this final chapter, we will compare the different isolation levels we introduced earlier as solutions to anomalies, based on the types of anomalies they address. While the first two chapters answered the question,
βWhich isolation levels can prevent a given anomaly?β,
this chapter will instead ask:
βWhich anomalies can a given isolation level prevent?β
In doing so, weβll summarize our earlier discussion while reinforcing the subject by approaching it from a reverse angle.
To recap: databases offer various isolation levels to preserve consistency between concurrent transactions that access the same data. Each level can prevent certain anomalies but may fall short in stopping others.
Okay then, let's explore the four main isolation levels in terms of the anomalies they address and their impact on system behavior.
π 1. Read Uncommitted
Description:
This is the lowest level. Transactions can read data that has not yet been committed.
Consistency Provided:
Almost none.
Anomalies Prevented:
Effectively none.
| Anomaly | Prevented? |
|---|---|
| Dirty Read | β |
| Non-Repeatable Read | β |
| Write Skew | β |
| Lost Update | β |
| Read Skew | β |
| Phantom Read | β |
Advantages:
- Highest performance
- Minimal locking
Disadvantages:
- High risk of data inconsistency
- Typically used only for read-only or analytical purposes
π 2. Read Committed
Description:
Only committed data can be read. This is the default isolation level in many databases.
Anomalies Prevented:
- Dirty Read
| Anomaly | Prevented? |
|---|---|
| Dirty Read | β |
| Non-Repeatable Read | β |
| Write Skew | β |
| Lost Update | β |
| Read Skew | β |
| Phantom Read | β |
Advantages:
- Balanced performance and consistency
- A reasonable starting point for most enterprise systems
Disadvantages:
- Data inconsistencies can still occur under complex concurrent operations
- Risks like lost updates and write skew remain
π 3. Repeatable Read
Description:
A transaction will see the same data every time it reads the same row. However, newly inserted rows are not covered by this rule.
Anomalies Prevented:
- Dirty Read
- Non-Repeatable Read
- Read Skew (in most systems)
| Anomaly | Prevented? |
|---|---|
| Dirty Read | β |
| Non-Repeatable Read | β |
| Write Skew | β |
| Lost Update | β (depends on the DB engine) |
| Read Skew | β (in some systems) |
| Phantom Read | β |
Advantages:
- High consistency for transactions operating on the same records
- Most read anomalies are prevented via row-level locking
Disadvantages:
- Phantom reads can still occur
- Higher performance cost compared to Read Committed
π 4. Serializable
Description:
This is the highest isolation level. All transactions are executed as if they were serialized.
Typically uses advanced mechanisms such as predicate locking or Serializable Snapshot Isolation (SSI).
Anomalies Prevented:
All major anomalies are prevented.
| Anomaly | Prevented? |
|---|---|
| Dirty Read | β |
| Non-Repeatable Read | β |
| Write Skew | β |
| Lost Update | β |
| Read Skew | β |
| Phantom Read | β |
Advantages:
- Highest level of data consistency
- Guarantees logical correctness
Disadvantages:
- High risk of locking and transaction conflicts
- Poor scalability and performance
π§ Decision Matrix: When to Use Which Isolation Level?
| Requirement | Recommended Level |
|---|---|
| Max performance, low consistency | Read Uncommitted |
| Basic consistency, good performance | Read Committed |
| Strong row-level consistency | Repeatable Read |
| Complex logical correctness, safety | Serializable / SSI |
β Conclusion
Data consistency is a foundational pillar for the reliability and predictability of modern software systems. In this three-part series, we explored real-world-inspired example scenarios to examine different data inconsistency anomalies, how they occur, and which isolation levels can prevent them.
Here are some clear takeaways:
- Different isolation levels trade off consistency at different points to improve parallelism and performance.
- Anomalies such as Dirty Read, Non-Repeatable Read, Phantom Read, Write Skew, Read Skew, and Lost Update are often overlooked but can drastically affect application behavior.
- These issues should not be evaluated solely at the database level; they require a broader view, involving application architecture, transaction management, and data modeling strategies.
π― Strategic Approach
- Locking your entire system into a single isolation level can either result in excessive rigidity or inadequate protection. Instead, make context-aware, requirement-driven isolation choices.
- When using an ORM, do not rely on default isolation levels. Clearly define critical transactions and support them with tools like
transaction propagationandlock hints. - In high-volume, multi-user, write-heavy systems, using Serializable by default can lead to scalability problems. However, for mission-critical financial or security-sensitive operations, it may be unavoidable.
π«‘ Final Words
Data consistency is not only the responsibility of database developersβitβs a concern for system architects, backend developers, and product decision-makers alike. Isolation levels are both a technical and a product-level decision.
I hope the topics covered in this series help you take a more informed and controlled approach to architectural decisions and critical data-related scenarios. Until the next post, take care.
π Recommended Reading
Some great resources Iβve benefited fromβand that you may also find useful:
Martin Kleppmann. Designing Data-Intensive Applications
https://www.oreilly.com/library/view/designing-data-intensive-applications/9781491903063/PostgreSQL Documentation β Transaction Isolation and Consistency
https://www.postgresql.org/docs/current/transaction-iso.htmlSQL Server Documentation β Isolation Levels in SQL Server
https://learn.microsoft.com/en-us/sql/t-sql/statements/set-transaction-isolation-level-transact-sqlHibernate ORM Documentation β Transaction and Concurrency Control
https://docs.jboss.org/hibernate/orm/current/userguide/html_single/Hibernate_User_Guide.html#locking