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How can you ensure consistency in distributed transactions?

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Distributed transactions are operations that involve multiple data sources, such as databases, services, or message queues, that need to be coordinated and synchronized. They are common in distributed systems, where data is replicated or partitioned across different nodes for scalability, availability, or performance reasons. However, distributed transactions pose a challenge for ensuring consistency, which means that the data remains valid and coherent according to some predefined rules or constraints. In this article, you will learn about some of the concepts and techniques that can help you achieve consistency in distributed transactions.

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1 ACID properties

One way to define consistency is to use the ACID properties, which stand for atomicity, consistency, isolation, and durability. These properties describe the desired behavior of transactions in a single data source, such as a relational database. Atomicity means that a transaction is either executed completely or not at all, without leaving any partial effects. Consistency means that a transaction preserves the integrity and validity of the data, according to some predefined rules or constraints. Isolation means that a transaction is executed as if it was the only one running, without being affected by or affecting other concurrent transactions. Durability means that the effects of a transaction are persistent and not lost due to failures or crashes.

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2 CAP theorem

However, achieving the ACID properties in distributed transactions is not trivial, because of the possibility of network failures, delays, or partitions. The CAP theorem, also known as Brewer's theorem, states that in a distributed system, it is impossible to guarantee all three of the following properties at the same time: consistency, availability, and partition tolerance. Consistency means that all nodes see the same data at the same time. Availability means that all nodes can respond to requests at any time. Partition tolerance means that the system can continue to operate even if some nodes are unreachable due to network failures. The CAP theorem implies that in the presence of network partitions, which are inevitable in distributed systems, you have to choose between consistency and availability.

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Paul Sweatte
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A tradeoff between consistency and availability needs to be made when the network becomes partitioned. Either you can choose to allow replicas to continue to serve queries, making availability the priority; or you can choose to stop accepting writes to the master, making consistency the priority. If not accepting writes has a larger financial impact, then choose availability.

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3 Consistency models

The CAP theorem reveals that there is no uniform solution for consistency in distributed transactions, and different systems thus adopt different consistency models. These models define the trade-offs and guarantees they provide. Strong consistency is the ideal model, corresponding to ACID properties, but it requires a lot of coordination and synchronization among nodes which can affect performance and availability. Eventual consistency is the weakest model, corresponding to availability, and allows transactions to execute without waiting for other nodes to confirm or acknowledge, improving performance and availability. However, this model can lead to conflicts and inconsistencies that need to be resolved later. Causal consistency is stronger than eventual consistency but weaker than strong consistency; it ensures that all nodes see the same data in the same causal order, but does not guarantee that all nodes see the same data at the same time or that transactions are executed atomically, consistently, isolatedly, or durably.

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Paul Sweatte
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A distributed transaction approach updates nodes synchronously as part of a single transaction, but the network latency involved in doing this needs to be planned for and measured to set expectations for both internal and external users.

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4 Consistency protocols

To implement different consistency models, distributed systems use various protocols and algorithms to coordinate and synchronize the nodes involved in distributed transactions. Two-phase commit (2PC) is one such protocol, which ensures atomicity and consistency in distributed transactions. This protocol involves two phases: a prepare phase and a commit phase. However, it has some drawbacks including blocking, fragility, and performance overhead. Another protocol is Paxos, which guarantees consensus and consistency in distributed systems by multiple rounds of communication among nodes. But this protocol is complex and difficult to implement and understand. Conflict-free replicated data types (CRDTs) are data structures that ensure eventual consistency in distributed systems by using commutative, associative, and idempotent operations. Examples of CRDTs include counters, sets, maps, and registers. Therefore, these protocols and algorithms can help ensure the accuracy of distributed transactions while avoiding conflicts or inconsistencies.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Paul Sweatte
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Removing the need to manually do anything, from configuration and partitioning to replication or failover, decreases the risk of human error, data loss, and downtime.

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How can you ensure consistency in distributed transactions?

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1 ACID properties

2 CAP theorem

3 Consistency models

4 Consistency protocols

5 Here’s what else to consider

Computer Engineering

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How can you ensure consistency in distributed transactions?

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1 ACID properties

2 CAP theorem

3 Consistency models

4 Consistency protocols

5 Here’s what else to consider

Computer Engineering

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