What is a Cloud-native Database?

Before this series is complete, I plan on defining in some detail what the various levels of Cloud-nativeness might be to allow readers to classify products based on architecture, not marketing. In this post, I’ll lay out some general concepts. First, let’s be real about cloud-things that are not cloud-native.

Any database that runs on physical hardware can run on virtual machines and, therefore, can run on virtual machines in the cloud. Databases with no cloud capabilities other than the ability to run on a VM on a cloud-provider are not cloud-native. Worse, there are lots of anecdotal stories that suggest that there are no meaningful savings to be had from moving a database from an on-premise server or VM to a cloud VM with no other change to take advantage of cloud elasticity.

So here are two general definitions for your consideration:

1) A cloud-native database will have one or more features that utilize capabilities found only on a cloud-computing platform, and

2) A cloud-native database will demonstrate economic benefits derived from those cloud-specific features.

Note that the way you pay for services via capital expenses (CapEx) or as operating expenses (OpEx) does not provide economic benefit. If the monetary costs of a subscription are more-or-less equal to the financial costs of a license, then savings are tied to tax law, not to economics. Beware of cloudy subscriptions that change how you pay without clearly adding beneficial cloudy features. It is these subscriptions that often are the source of the no-savings anecdotes mentioned above.

This next point is about the separation of storage from compute. Companies have long ago disconnected their databases from just-a-bunch-of-disks (JBOD) to shared storage such as SAN or S3. Any database today can use shared storage. It is not useful to say that any database that can use shared storage has separated storage from compute. Using the idea that there must be features, not marketing, that allow compute to scale separately and more-or-less dynamically from storage as the definition, we will be able to move forward in this area.

So:

3) For storage to be appropriately separated from Compute, it must be possible to scale Compute up and down dynamically.

Next, when compute scales, it scales at different granularity. An application or database that automatically adds and subtracts virtual machines provides different economics than a database that scales using containers. Apps that add and subtract containers have different economics than applications that use so-called serverless containers to scale. In this dimension, we will try to characterize granularity to account for the associated cloud economics. This topic will be covered in more detail later, so I’ll save the rule for that post.

Note that it is possible for database vendors to develop a granular architecture and to use the associated economics to their advantage. They may charge you for the time when any part of your database is running but be billed by their provider for smaller chunks. This is not an issue unless their overall costs become uncompetitive.

Last, and in some ways least, different products may charge for time in smaller or larger chunks. You might be charged by the hour, by the minute, by the second, or in smaller increments. Think about the scaling economics I suggested in the first posts of this thread. If you are charged by the hour, then there is no financial incentive to scale up to finish jobs to the minute. You will be charged the same for ten minutes or fifty minutes when you are charged by the hour. The rule:

4) Cloud databases that charge in smaller time increments are more economical than those that charge in larger increments.

The rationale here is probably obvious, but I’ll cover it in-depth in a later post.

With these concepts in place, we can discuss how architectural changes affect each aspect of the economics of databases in the cloud.

Note that this last sentence was written assuming that a British computer scientist with an erudite accent would speak it when they create the PBS series from these posts. Not.

By the way, a few posts from now, I am going to go back to some ideas I shared five years ago around the relationship between database processing and the underlying hardware platform. I’ll update this thinking with cloud computing in mind. You can find this thinking here which originally came from Jeff Dean and Peter Norvig (displayed in lots of places but here is one).

The Big Data Devil

Devil
Devil (Photo credit: Wikipedia)

I just finished a draft for next week on Big Data and thought that with this note I might form a preface…

First… Big Data is about, well…, Big Data. When Gartner devised the three V’s I suspect that they were trying to frame the new stuff that was emerging… not establish a concise definition. So let me be very clear about what I think that Big Data is and is not.

Big Data is about volume, not velocity, not variety. That is what the words “big” and “data” conjoined must mean. Velocity + Volume is Big Data. Variety + Volume is Big Data. By themselves Velocity and Variety are new, important, separate, technological trends.

Next, Big Data is a new thing. It is not a technology that was around in a meaningful way 5+ years ago. It was emerging just then so we should see evidence in the advances offered by the Web Scale companies like Google, Yahoo, and Netflix. It is not any data that was conventionally created, captured, or used before 2010 or so.

So what is new, big, and was emerging in recent history? It is the creation, capture, and use of machine-generated data: click-stream data, system log data, and sensor data. Big Data technology has to do with the creation, capture, and utilization of large volumes of machine-generated data… nothing more or less.

Rob: Big Data legitimately includes Social Data as well as Vitaliy rightfully commented… I’ll post on this soon…

Machines generate data at a very low-level of detail. It is said that the devil is in the detail… and the subject of the next post deals with the notion that in order to make our companies more profitable¬†we must all chase this damnable devil.

PS

I wonder if damnable devil is redundant? Probably, yes.

2nd PS (sort of like 2nd breakfast)

Big Data is not about any and every new technology introduced in the last five years…

More on The Future of Hadoop and of Big Data DBMSs

 

First, you should look at Google’s Spanner paper here… this is the next-gen from Google and once it is embraced by the open source community it will put even more pressure on the big data DBMSs. Also have a look at YARN the next Map/Reduce… more pressure still…

Next… you can imagine that the conventional database folks will quibble a little with my analysis. Lets try to anticipate the push-back:

  • Hadoop will never be as fast as a commercial DBMS

Maybe not… but if it is close then a little more hardware will make up the difference… and “free” is hard to beat in price/performance.

  • SSD devices will make a conventional DBMS as fast as in-memory

I do not think so… disk controllers, the overhead of non-memory I/O, and an inability to fully optimize processing for in-memory will make a big difference. I said 50X to be conservative… but it could be 200X… and a 200X performance improvement reduces the memory required to process a query by 200X… so it adds up.

  • The Price of IMDB will always be prohibitive

Nope. The same memory that is in SSD’s will become available as primary memory soon and the price points for SSD-based and IMDB will converge.

  • IMDB won’t scale to 100TB

HANA is already there… others will follow.

  • Commercial customers will never give up their databases for open source

Economics means that you pay me now or you pay me later… companies will do what makes economic sense.

The original post on this is here

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