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Consistency Levels in Storage Replication


Distributed systems, particularly distributed data stores, offer different consistency levels for various operations initiated by clients. The consistency level of a system defines the ordering and visibility of operations in the system.

Knowing the consistency level of a distributed system can help users design their applications, as well as understand the scenarios in which operations initiated by them can succeed or fail.

Several consistency levels exist in the world of distributed systems, but in the context of messaging systems like BlazingMQ, only two consistency levels are worth discussing – Eventual Consistency and Strong Consistency. The former is sometimes also referred to as Weak Consistency.

The rest of this section describes eventual and strong consistency levels in the context of BlazingMQ, compares the two consistency levels and also goes over some additional considerations, which will help BlazingMQ users choose the correct level of consistency for their BlazingMQ domains.

Eventual Consistency

For a queue that is configured with the eventual consistency level in BlazingMQ, when the primary node of the queue receives a PUT message, it writes the message to its local storage, asynchronously sends it to the replicas, and immediately sends the ACK back to the producer application, without waiting for any confirmation from the replicas.

This configuration relies on the fact that the message will eventually reach the replicas, which will then apply and store the message in their local storages respectively.

Readers may have noticed that there is a chance of message loss in eventual consistency mode. This can occur when the primary node crashes (or goes down gracefully) immediately after sending the ACK to the producer, but before the message could be replicated to all or to the majority of the replicas (the message could still be in the user or kernel space TCP socket buffers). Additionally, it is also possible for the replicated message to get lost because of a network split etc, in which case replicas will not receive the message. This is the risk that comes with eventual consistency.

However, the latency numbers for eventually consistent queues are very attractive. Since a primary node performs minimal work between receiving a PUT and sending an ACK, the producer application can expect the ACK fairly quickly. Additionally, consumers will also see the corresponding PUSH message earlier, because the primary node sends it right away to the consumer application, without waiting for acknowledgements from replicas.

So when choosing an eventual consistency level, BlazingMQ users should know that while this consistency level will give low latency numbers, there is a non-zero probability of a message getting lost.

We recommend using eventual consistency only in exceptional circumstances.

Strong Consistency

A strong consistency level in BlazingMQ solves the problem of potential message loss, which can occur in eventual consistency mode as described above. This is done by ensuring that the primary node waits for acknowledgements (known as Replica Receipts in BlazingMQ lingo) from a sufficient number of replicas before sending ACK and PUSH messages to the producer and the consumer applications respectively.

This ensures that even if the primary node crashes after sending an ACK message, replicas are guaranteed to have the PUT message, and as one of the replicas gets promoted to the role of primary, it will be able to send that message to the consumer application.

The number of replicas which must send a receipt to the primary node for a message to be considered “committed” is derived from the size of the BlazingMQ cluster. In a cluster having 4 nodes, the primary node will wait for receipts from 2 replicas, which will ensure that the message ends up in at least 3 nodes (2 replicas plus 1 primary). Similarly, in a cluster of 6 nodes, the primary node will wait for receipts from 3 replicas, thereby ensuring that the message has been stored in at least 4 nodes. Generally speaking, to ensure strong consistency, BlazingMQ ensures that a total of N/2 + 1 nodes in the cluster have recorded the message, where N is the total number of nodes in the cluster.

While strong consistency provides higher message guarantees, it can come with some latency overhead. However, we have introduced several optimizations in the strong consistency implementation in BlazingMQ, and our tests have indicated that latency numbers for strong consistency are only slightly higher than the ones for eventual consistency.

Some of the optimizations in the implementation include:

  • Replicas sending a receipt message to the primary for a batch of messages, instead of a receipt for every message.

  • Replicas further optimizing the receipt code path by taking into consideration if the link between the replica and the primary is overloaded, and trying to conflate the receipt messages.

Other Considerations

Timeouts at Primary Node

Since the primary node waits for replicas to send replication receipts, it is possible for this operation to time out due to widespread BlazingMQ cluster or network issues. In such scenario, the primary node will issue a negative ACK with an UNKNOWN status to the producer application, and will give up on delivering this message to the consumer(s). Note that the message can still be delivered in the scenario where the current primary node goes down and a replica which received the message (and perhaps even sent its receipt) gets promoted as the new primary. In such case, the new primary will send the message to the consumer. Hence the UNKNOWN status in negative ACK. This timeout is configurable.

Flushing Messages to Disk

Note that BlazingMQ does not flush messages to disk in either consistency level before sending an ACK to the producer. Instead, it relies on the OS to do so periodically.