Understanding database isolation levels is crucial for developers who need to manage concurrent transactions effectively. Different isolation levels provide various guarantees about how transactions interact with each other, which can impact both the performance and correctness of your applications. Here, we’ll explore the four standard isolation levels defined by the SQL standard—Read Uncommitted, Read Committed, Repeatable Read, and Serialisable - and discuss how to implement these in PostgreSQL and Django.

What Are Database Isolation Levels?

Isolation levels define the degree to which the transactions are isolated from each other, controlling how and when the changes made in one transaction become visible to others. They are a key component of the ACID (Atomicity, Consistency, Isolation, Durability) properties that ensure transactional integrity within relational databases.

1. Read Uncommitted

Description: This is the lowest level of isolation. In this level, transactions may see changes made by other transactions even before those changes are committed, leading to phenomena known as “dirty reads.”

Sample SQL (PostgreSQL):

BEGIN TRANSACTION ISOLATION LEVEL READ UNCOMMITTED;
SELECT * FROM orders WHERE customer_id = 101;
COMMIT;

Use Case: Read Uncommitted is generally not recommended but can be used in situations where read speed is prioritised over accuracy, such as in certain types of data analytics where fresh, albeit possibly inaccurate, data can be tolerated.

2. Read Committed

Description: This isolation level ensures that a transaction can only see data committed before it started. Changes applied by other transactions after it began are not visible, thus preventing dirty reads.

Sample SQL (PostgreSQL):

BEGIN TRANSACTION ISOLATION LEVEL READ COMMITTED;
SELECT * FROM orders WHERE customer_id = 102;
COMMIT;

Django ORM Example: Django uses Read Committed as the default isolation level for most databases. It’s ideal for applications where seeing committed data is necessary for consistency but perfect isolation is not required.

Use Case: Suitable for most applications such as web applications where seeing only committed data is necessary to ensure the application’s state consistency, as it provides a good balance between consistency and performance.

Scenario: Suppose we want to ensure that we only process an order if the item stock is sufficient. We check the stock, process the order, and then update the stock. These operations need to be atomic to prevent other transactions from purchasing the same stock simultaneously after it’s been checked but before it’s updated.

from django.db import transaction

def process_order(customer_id, product_id, quantity):
    with transaction.atomic():
        stock = Product.objects.select_for_update().get(id=product_id)

        if stock.quantity >= quantity:
            # Proceed with order
            Order.objects.create(customer_id=customer_id, product_id=product_id, quantity=quantity)
            stock.quantity -= quantity
            stock.save()
            return "Order processed successfully"
        else:
            return "Not enough stock"

# Assuming a view or function calls this:
result = process_order(102, 501, 2)

transaction.atomic() ensures that checking the stock and updating it after creating the order happen within a single transaction. The select_for_update() locks the stock row to prevent other transactions from modifying it until the current transaction completes, which is crucial in a Read Committed scenario to prevent “lost updates”.

The term “lost updates” refers to a specific type of concurrency issue in database systems where changes made by one transaction are overwritten by another transaction, without the latter being aware of the former’s changes. This can lead to data inconsistencies and logical errors in database applications.

Lost updates typically occur in scenarios where two or more transactions read the same data and then update it based on what was read. If these updates are made without proper locking mechanisms or transaction isolation, the last update can overwrite changes made by the first, essentially “losing” these updates. Here’s a simple example to illustrate this:

  1. Transaction A and Transaction B both read a row from the accounts table that shows a balance of £100.
  2. Transaction A modifies the balance to £120 and commits the change to the database.
  3. Transaction B, unaware of the recent update by Transaction A, still operates on the old balance it read (£100) and updates the balance to £110, then commits.
  4. The final balance in the account is now £110, not £120 as intended by Transaction A or £130 as might be expected if both transactions’ updates were considered. The update by Transaction A is lost.

Lost updates can be prevented by employing appropriate locking strategies or choosing the correct transaction isolation levels. Here are a few common methods:

  1. Pessimistic Locking: This involves locking the data so that no other transactions can read or write to the data until the lock is released. In SQL, this is often done using SELECT FOR UPDATE, which locks the selected rows.
  2. Optimistic Locking: Instead of locking the data at the database level, this method involves tracking changes through a version number or timestamp. Each row might have a version number, and when updating data, the transaction checks if the version number has changed since it was last read. If the version number has changed, the update is aborted because another transaction has modified the row.
  3. Higher Isolation Levels: Setting the transaction isolation level to REPEATABLE READ or SERIALIZABLE can help prevent lost updates. REPEATABLE READ ensures that if a row is read twice in the same transaction, the same data is returned each time, thereby preventing lost updates within the transaction. SERIALIZABLE goes further, essentially sequencing transactions so that one completes fully before another can interact with the same data.

3. Repeatable Read

Description: This level guarantees that any data read during a transaction cannot change if read again during the same transaction, even if changes are committed by other transactions. This prevents non-repeatable reads but does not necessarily prevent phantom reads.

Sample SQL (PostgreSQL):

BEGIN TRANSACTION ISOLATION LEVEL REPEATABLE READ;
SELECT * FROM orders WHERE customer_id = 103;
-- This query will always return the same result within the transaction
COMMIT;

Django ORM: To use Repeatable Read in Django, you would typically need to configure your database connection in settings.py or adjust your database session settings directly through raw SQL.

from django.db.backends.postgresql.psycopg_any import IsolationLevel

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.postgresql',
        'NAME': 'your_database_name',
        'USER': 'your_database_user',
        'PASSWORD': 'your_database_password',
        'HOST': 'your_database_host',
        'PORT': '',
        'OPTIONS': {
            'isolation_level': IsolationLevel.REPEATABLE,
        },
    }
}

Use Case: Useful in applications like financial reports or user account balances where a consistent view of the data must be maintained throughout the transaction.

4. Serialisable

Description: This is the highest level of isolation. It ensures complete isolation from other transactions, effectively serialising access to the affected data. Transactions are virtually serialised, so they appear to be happening one at a time.

Sample SQL (PostgreSQL):

BEGIN TRANSACTION ISOLATION LEVEL SERIALIZABLE;
SELECT * FROM orders WHERE customer_id = 104;
-- Guarantees full isolation
COMMIT;

Django ORM: Configuring Serialisable isolation in Django would again involve setting the isolation level via the database configuration in settings.py or using raw SQL commands.

from django.db.backends.postgresql.psycopg_any import IsolationLevel

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.postgresql',
        'NAME': 'your_database_name',
        'USER': 'your_database_user',
        'PASSWORD': 'your_database_password',
        'HOST': 'your_database_host',
        'PORT': '',
        'OPTIONS': {
            'isolation_level': IsolationLevel.SERIALIZABLE,
        },
    }
}

Use Case: Essential in high-stakes financial transactions or other critical operations where even the slightest chance of anomalies cannot be tolerated.

Conclusion

Understanding and correctly implementing the appropriate isolation level can significantly enhance the reliability and performance of your database operations. For Django developers, while the framework does not allow setting isolation levels directly in the ORM for each transaction, setting these via the database settings or using raw SQL are viable approaches. Properly handling isolation levels ensures your application can handle concurrent operations safely and efficiently.