Implementing Linearizable Consistency in Cassandra with Lightweight Transactions

The Problem: Preventing Race Conditions in Distributed Writes

In a standard Apache Cassandra write, the system follows a "last-write-wins" (LWW) conflict resolution strategy based on timestamps. While this ensures high availability and low latency, it creates race conditions when a write depends on the current state of the data. For example, if two clients attempt to create a user account with the same email address simultaneously, both writes may succeed, leaving the database in an inconsistent state.

The solution is Lightweight Transactions (LWTs). LWTs provide linearizable consistency—the strongest consistency guarantee—by ensuring that a write is only executed if a specific condition is met, effectively turning a write into a "Compare-and-Set" (CAS) operation.

How LWTs Work: The Paxos Consensus

Unlike standard writes that propagate data to replicas and return success based on a consistency level (like QUORUM), LWTs use the Paxos consensus protocol. This process requires four round-trips between the coordinator node and the replicas to agree on the proposal before the data is permanently committed.

The process follows these phases:

• Prepare/Promise: The coordinator proposes a sequence number. Replicas promise not to accept any proposal with a lower number.
• Read/Proposed: The coordinator reads the current state to verify if the IF condition is still true.
• Accept/Acknowledge: The coordinator proposes the new value. Replicas accept it if the sequence number is still valid.
• Commit: Once a quorum of replicas acknowledges the proposal, the coordinator commits the write.

Practical Implementation: Compare-and-Set

LWTs are implemented in CQL using the IF EXISTS or IF NOT EXISTS clauses, or by specifying a conditional value check. These operations must be executed via cqlsh or a driver supporting the SERIAL consistency level.

Example: Unique Username Registration

Assume a table defined as follows:

CREATE TABLE users (
    username text PRIMARY KEY,
    email text,
    created_at timestamp
);

To ensure a username is not overwritten by a second registration attempt, run the following command on the coordinator node (via cqlsh):

INSERT INTO users (username, email, created_at) 
VALUES ('tech_editor', 'editor@example.com', toTimestamp(now())) 
IF NOT EXISTS;

Expected Result: Cassandra returns a result set containing a column named [applied].

• If the username did not exist, [applied] is True.
• If the username already existed, [applied] is False, and the current row data is returned so the application can handle the conflict.

Performance Trade-offs and Limitations

LWTs are not a general replacement for standard writes. They introduce significant overhead and architectural constraints:

1. Latency Penalty

Because Paxos requires multiple round-trips, an LWT is substantially slower than a standard write. In a typical cluster, an LWT can be 3x to 5x slower than a write with CONSISTENCY QUORUM. Use them only for critical operations like account creation or state transitions, not for high-frequency telemetry or logging.

2. Partition Scope

LWTs are scoped to a single partition. You cannot perform a linearizable transaction that spans multiple tables or multiple partition keys. If you need atomicity across partitions, you must implement a Saga pattern or use an external orchestration layer.

3. Contention and Timeouts

When multiple clients attempt to update the same partition using LWTs simultaneously, they compete for the Paxos proposal. This leads to contention, which increases the likelihood of WriteTimeoutException. If your workload involves high-frequency updates to a single row, LWTs will likely become a bottleneck.

Configuration and Verification

To verify the behavior of LWTs in your environment, you can monitor the Paxos-specific metrics via JMX. Look for Paxos related timeouts and latency spikes during peak load.

Consistency Level Mapping

LWTs use a dual-consistency model. While the final write may use LOCAL_QUORUM, the consensus phase uses a special consistency level called SERIAL (or LOCAL_SERIAL for single-datacenter consensus). Ensure your client driver is configured to handle these levels.

Operation Type	Consistency Level	Guarantee
Standard Write	QUORUM / LOCAL_QUORUM	Eventual/Tuned Consistency
LWT (CAS)	SERIAL / LOCAL_SERIAL	Linearizable Consistency

Verification Step

To test the failure state, execute the same INSERT ... IF NOT EXISTS command twice. The first should return [applied]: True, and the second must return [applied]: False. If the second write succeeds, your table schema or consistency settings are not correctly enforcing the transaction.