Linearizability vs Serializability

The field of distributed systems often leaves software engineers juggling with complex terms like Linearizability and Serializability. While they deal with similar aspects of system operations, they are fundamentally different.

Overview Comparison Table

DefinitionGuarantees that each operation appears instantaneously, exactly once, at some point between its start and end times.Guarantees complete isolation between multiple transactions, that is, the outcome of any execution is the same as if the operations of the transactions were executed in some sequential order.
ContextOften used in context of single operation on a single object.Concerns transactional (multi-operation, multi-object) settings.
Real-time ConstraintProvides real-time (i.e., wall-clock) guarantees.Does not provide real-time ordering.
Example of UsageAtomic fetch-and-increment instruction in CPU.Database transactions maintaining ACID properties.

The main difference between Linearizability and Serializability is that Linearizability guarantees that each operation on a single object appears instantaneously, exactly once, at some point between its start and end times, often providing real-time guarantees, whereas Serializability ensures complete isolation between multiple transactions (multi-object operations) providing the same outcome as if these operations were executed sequentially, but does not offer real-time guarantees.

What is Linearizability?

Linearizability, in the context of distributed systems, is a guarantee about single operations on single objects. It ensures that there is a specific point in real time where each operation appears to occur, exactly once. In simple words, linearizability is like being in a single queue at the fast food counter; the server attends to one customer at a time, in the order they arrived. This makes linearizability a very intuitive model, as it's closely related to how things work in the physical world.

Examples of Linearizability

Say, for instance, you're working with a simple counter in a multi-threaded system. If two threads are incrementing the counter simultaneously, a linearizable system would ensure that, from the perspective of an observer (another thread or process), one increment happens strictly before the other, and the counter ends up with the correct value.

To elaborate the concept further, let's take another example - consider a distributed data store that supports append operations. Two separate clients are appending to the same data store: one client appends 'A,' and the other 'B.' In a linearizable system, the data store will end up with either 'AB' or 'BA', but not something like 'AA' or 'BB.'

This shows how linearizability provides a simple and intuitive consistency model, making the design of distributed systems easier for application developers.

What is Serializability?

Serializability represents a higher level of order in computer systems. It's all about keeping things neat when multiple transactions (groups of operations) are happening at the same time. Serializability ensures that these transactions play out as if they happened one after another, not all at once, maintaining the system's correctness.

This doesn't mean they physically occur one by one - they can still happen all at once. But the final result will be the same as if they happened sequentially.

Examples of Serializability

If we think about a banking system where you're trying to transfer money to a friend, several steps need to happen. The bank has to check your account, subtract the money, then add it to your friend's account. In a serializable system, those actions together form a transaction and will appear to happen all at once. Serializability guarantees that no other transaction (like someone else also transferring money at the same time) messes up this sequence of operations.

As another example, let's consider an inventory management system. Here, a transaction would encompass checking if an item is available, decrementing the stock count after a sale, and confirming the order. Under serializability, these operations would appear as one single action, ensuring no mistakes (like selling more items than in stock) happen regardless of simultaneous transactions.

Thus, serializability helps to mirror a real-world sequence of events, ensuring that databases remain consistent and correct even under many concurrent transactions.

When to Use Linearizability

Linearizability really shines in systems where strong consistency and real-time guarantees are necessary. It simplifies system design as it imposes a strong order on operation outcomes, making it easy to reason about system behavior.


Let's take a look at some scenarios where linearizability would be a good fit:

Scenario 1: A Distributed Counter

Imagine a system with a distributed counter that multiple users are incrementing simultaneously. Here, a linearizable store ensures that each increment is processed completely before the next, and the counter reflects a consistent, correct value.

In pseudo code, an increment operation in a linearizable system would look like this:

def increment_counter(): current_value = counter.get() # Get current value of the counter counter.set(current_value + 1) # Increment the counter

In a linearizable system, no two increment operations could interleave, thus preventing any potential race-condition scenario.

Scenario 2: Leader election in Distributed System

Another place where linearizability comes in handy is leader election in distributed systems. While several nodes may attempt to become a leader simultaneously, linearizability ensures that only one of them succeeds.

Though ideal for scenarios that need strong consistency, using linearizability does have negative implications on performance. Hence, it's essential to balance your system's consistency needs with available resources and specific use-cases.

When to Use Serializability

Serializability is best used in systems where maintaining the order and integrity of a group of operations is more important than achieving higher performance and speed.


Here are some scenarios where serializability might be essential:

Scenario 1: Transactions in Banks

Banking transactions are a great example. If multiple transactions are happening on an account simultaneously, serializability guarantees that they happen in some sequence, maintaining the correctness of the account balance.

Let's look at a simple pseudocode for a banking transaction:

def transfer(from_account, to_account, amount): if from_account.balance >= amount: from_account.balance -= amount to_account.balance += amount else: print("Insufficient balance!")

In a system providing serializability, even when multiple transfer() transactions are executed concurrently, the end state would be as if the transactions occurred in some sequential order, preserving the correctness of the account balances.

Scenario 2: Inventory Management Systems

Similarly, in an inventory management system, when selling a product, the system must verify that the product is in stock, allocate it, and finally confirm the order. Serializability would ensure that these operations will not be interrupted by other transactions, preventing over-selling or under-selling.

Though beneficial for systems that require these transaction sequence guards, remember that using serializability can negatively impact performance, especially in large-scale distributed systems. Therefore, it's crucial to consider the trade-offs associated with its usage based on your specific application needs.

Key Takeaways

Linearizability and serializability, despite somewhat similar names, are fundamentally different.

  • Linearizability is a guarantee about single operations on single objects. It is an excellent choice when dealing with systems where real-time ordering and strong consistency are essential. It works perfectly for individual operations and offers instantaneous action from the perspective of all threads. However, it can affect system performance due to the necessary synchronization and coordination.

  • Serializability, on the other hand, is a high-level of isolation that manages groups of operations — transactions. It guarantees that the final state of the system would be the same as if transactions had executed one after another, ensuring correctness. But, it can reduce the system's efficiency due to sequencing constraints.

Frequently Asked Questions About Linearizability and Serializability

Let's go through some of the frequently asked questions related to these concepts to dispel any confusions and misunderstandings.

What Are Some Misunderstandings about Consistency Guarantees?

One common misunderstanding about consistency guarantees like Linearizability and Serializability is the idea that they always negatively affect system performance. While they do introduce overhead due to synchronization and coordination, the trade-off often leads to increased system reliability and design simplicity. Another misconception is that they involve sequencing operations physically, when in actuality, they only control the perceived order of operations.

What Is an Execution That Is Serializable but Not Strict-Serializable?

A system's execution could be Serializable but not Strict-Serializable in situations where it maintains overall transaction consistency but doesn't offer real-time guarantees. For example, A distributed system might execute two transactions (T1 and T2) such that T1 completes before T2 starts (real-time order), but they are serialized as T2 before T1 (logical order). This situation is Serializable (as there is a valid serialization order) but not Strict-Serializable (as the logical order doesn't match the real-time order).

How Do Ordering Guarantees Play into Linearizability and Serializability?

Ordering guarantees are central to both Linearizability and Serializability. Linearizability insists on preserving the real-time order of operations: if a client sees operation A complete before B starts, then A must also precede B in the system's sequential history. Serializability, on the other hand, only requires some serial (sequential) order for transactions' operations, without the need for this order to obey real-time. Thus, ordering guarantees form the basis of these consistency models, helping ensure either strong real-time consistency (Linearizability) or transaction-level consistency (Serializability).