An Overview Of Consistency Levels In Enterprise Database Systems

Database systems may typically give users the power to trade-off between performance and correctness. There may be other categories to correct to trade for performance and consistency levels in the distributed database systems, etc. In this article, we will discuss an overview of consistency levels in enterprise database systems. So keep reading.

An increasing number of distributed database systems are now giving the uses various consistency levels to choose from and allow them to specify which consistency-based guarantees are needed from the system for different applications.

Similar to various isolation levels, consistency levels become performance standards and should come with the same type of features to reduce isolation levels. This blog will have a short overview of consistency levels to explain what they do and how they work.

Much of these existing consistency levels are done in the context of multiprocessor systems, which operate on a single data item at a time without the concept of a transaction. This article of an overview of consistency levels in enterprise database systems may give some directions on how to think about the consistency level based on acid compliance.

Also, you can check: An Overview Of Database Systems vs File Systems.

About Consistency Level

Consistently is a dependent context. In general, consistency may refer to the given system’s ability to ensure that it complies with a set of rules as predefined. However, these rules may change based on the given context. For example, C of ACID and C of CAP may both refer to uniformity.

However, the custom set of rules implies by these two different concepts are unmatched. In terms of ACID, the rules may refer to application-defined semantics.

The systems which guarantee ACID shows that transaction processing may not violate any referential integrity, constraints related to foreign keys, or any other specific application constraints.

On the other hand, C for CAP may refer to the rules related to making a concurrent system, which appears single-threaded and centralized. It may only have one possible result to read at a particular point, and it must reflect the most recent completed writes on the data item.

Another point of confusion here may be that we can eliminate the phrase consistency level, which is not typically used in the context of acid consistency. This is basically because the C of ACID is entirely the responsibility of application developers.

Only the developer can ensure that the code they place inside the transaction may not violate any application semantics. To assess your ACID compliance needs and set up a database accordingly, avail services of providers like RemoteDBA.com.

However, while we are talking about the consistency level, we refer to the C of CAP. Based on this, perfect consistency may usually be referred to as strict consistency.

No matter where the writes are performed, any consistency level below the perfect consistency may enable situations to arise where the reads do not return the most recent write of the data items. C of CAP refers to something called atomic consistency.

This is comparatively weaker than severe consistency, as discussed above, but it can still be considered perfect in practical scenarios. Based on how a particular system is architected, perfect consistency may become easier to achieve.

In inadequately designed systems, getting precision may come with the probability of performance and availability. The uses of these systems are pushed to accept the guarantees short of perfection.

However, even in well-designed systems, some non-trivial performance benefits may often be achieved by accommodating surety short of perfection.

An Overview Of Consistency Levels

The notion of consistency is originated from the research of shared memory and multiple processor systems. This early work’s goal was based on the reason about how and when the threads of execution, which may concurrently be accessing overlapping sets of data in the shared memory.

As such, most of this initial work was focused on the reads and wrote of the data items rather than the levels of a group of reading and writing within the transactions.

In terms of sequential consistency, all writes, no matter which thread made the writes and normatively which data items are written, are globally ordered. Every execution may have writes occurring in this order.

Sequential consistency places a requirement as to how to order the writes. In contrast to sequential consistency, strict consistency may place a real-time requirement on how it orders the writes.

It assumes that it may be possible always to know which time it currently is with a zero error. Each aspect of implementation affirms this accurate current time. Order of writes in sequential consistency order may be equal to the real-time which these writes issue.

Furthermore, each read operation may correspond to the value of the most recent write in real-time, no matter which thread of execution is initiated based on the write.

It is impossible to practice a global agreement on any precise current time in a given distributed system, which renders strict consistency to be mostly theoretical.

In the case of distributed systems, the writes and reads may originate anywhere. The highest level of consistency may be obtained in linearizability, which is also known as atomic consistency.

Atomic consistency is similar to strict consistency in theory. Both these are an extension of the sequential consistency, which imposes real-time constraints on the writes.

The major difference between these is that the atomic consistency model may acknowledge that there is a certain time between and operations submitted to the system and when it responds with an acknowledgment as it is completed.

In the distributed model, sending a written request to the write location may include replication, and it can further occur during this time. Atomic consistency guarantees not to place any ordering constraints on the operations, which occur with overlapping start and end.

The only ordering constraint here is for operations that do not overlap, and only in such cases does the early write may have to be seen before later rights.

The other two types of consistencies are casual consistency and eventual consistency. You need to consider all these in light of your data’s nature and the ACID compliance requirements to develop a consistent architecture.

Implement what you feel is suitable to your requirements to get the best out of it.

So that’s all from this article of an overview of consistency levels in enterprise database systems. Thank you for reading.