Choosing the Right Fit—Immediate or Eventual Persistence?
Choosing the Right Fit—Immediate or Eventual Persistence?
Depending on what your database handles, the D in ACID might not be completely necessary. See when immediate and eventual persistence can come in handy.
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With the emergence of NoSQL databases, "eventual persistence" is an option available to accelerate reads and writes to the database. Persistence, also popularly known as durability to disk, has long been recognized and cherished as one of the desirable characteristics of an ACID (Atomicity, Consistency, Isolation, and Durability) compliant database system.
When I first heard of eventual persistence, it did rattle my strong relational database mindset, where durability is almost a religious belief.
Over time, I have come to realize not all applications need immediate persistence, especially when the trade-off is performance and cost because reads from and writes to disk are slow. The advantages of eventual persistence sometimes greatly outweigh immediate persistence.
It is time to exorcise the fear of eventual persistence out of the database users!
Immediate and Eventual Persistence — What is the Difference?
Simply put, eventual persistence means that every time you write data into the database, it appears as if the row has been successfully written to disk because the write is acknowledged by the database system. The host triggering this write gets an acknowledgment back from database stating that the write is successful. However, under the covers, the database has in actuality not written the data to disk but has written it to an intermediate layer like memory or a file system cache. The actual write to disk is queued up and happens asynchronously.
Immediate persistence means the write to disk is synchronous and the write is acknowledged only after the data is written to disk.
A few seconds after this is mentioned to 3NFers (my name for relational database users ), the meeting ends very rapidly, or database solutions that provide eventual persistence are dismissed as unviable solutions. Acknowledgment of writes after they are persisted to disk has been the only way.
This blog examines if certain workloads can function with a deferred write to disk in favor of performance and cost IF the initial write is to a dependable, fully redundant failsafe layer with several layers of high-availability options.
Factors to Consider When Choosing the Right Persistence Option
I think we can, in many scenarios, get by with eventual persistence in favor of performance and cost. Let us examine it.
Before I delve any deeper into this, let me categorically state that the durability is a great feature, but it comes at the cost of performance and higher CAPEX/OPEX. Higher CAPEX/OPEX because there will be a need to invest in faster storage in order to deliver better performance. If you can get by because you have other redundancy features built into your product and performance is a huge consideration, then you have to consider eventual persistence as a possible solution.
Let us get back to the basics for a second and examine what an application user really wants from their interaction with a database.
- Write data quickly.
- Read the data I have written every time with consistent answers.
- Never lose what I have written. What this actually means is "minimize data loss." Observe that I use the word minimize because there are several scenarios where even the "iron-clad" guarantee of RDBMS databases can fall apart.
RDBMS databases have been around for a long, long time. They have certainly minimized and addressed these errors. It makes a whole lot of sense for very sensitive workloads that could result in lost opportunities caused by inconsistent results. My point is that it is an all-or-nothing solution. For applications that really don't need this kind of durability, the trade-off over performance proves to be an expensive one.
What if your database solution lets you write data quickly, consistently read what you have read, never lose what you have written, provide transaction isolation without sacrificing speed and performance at a fraction of the cost of what you currently have on the floor? Would that resonate with you? I believe so.
What if your database solution is:
- Memory-centric which lets you write data very fast.
- Keeps your data in memory, which enables you to read from memory very fast, essentially letting you consistently read what you have written.
- Has built HA features which lets you have copies of data in a rack-aware, datacenter-aware implementation, so that if a node goes down before a write could be persisted to disk, you could always rely on other nodes or another cluster to pick up the slack.
The advantages of this architecture is that:
- Applications that can tolerate some very, very rare cases of data being dropped can still function because there are several levels of redundancy built in.
- Speed and performance are not sacrificed.
- Cost of the solution is low.
Can Eventual Persistency Ensure There Is Never Any Data Loss In a System, Be it Relational or NoSQL?
The answer is actually no because you can only minimize the numbers of errors, not completely eliminate them. While many will argue that writing to disk is the safest way to protect data, having come from this world, I have seen and repaired several scenarios where data was lost due to bad drives, data corruption, failed ETL jobs, etc. Existing solutions do not necessarily provide a 100% guarantee that your data is safe. The point is we have to consider the various dimensions involved and the trade-offs we have to make.
If you choose eventual persistence, then it is imperative to ensure:
Built-in, inter-cluster, high availability features to compensate for data loss caused by not being able to persist data.
Several layers of redundancy either via cross cluster HA features or replication to alternate data centers.
Robust QA processes that can quickly detect and repair erroneous or lost data.
Performance, speed, and cost are paramount to your business.
Durability is desirable, but depending on the latency requirements, the application profile, and expense one is willing to incur, we could get by with eventual persistence. If your database solution has the following features:
- Has built-in redundancy and multiple levels of high-availability features that minimizes data loss and...
- ...can provide blazing performance at a fraction of the cost of the solution you currently have
Then eventual persistence is a more than a viable solution.
The chances that an application will experience data loss in a fully durable system is almost as much as it would be in an eventually persistent system with the additional overhead of cost, performance, and scalability. In that case, isn't durability overrated?
Published at DZone with permission of Sandhya Krishnamurthy , DZone MVB. See the original article here.
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