Mobile Clients Require High Performance BI Computing

English: An image of an iPad 2.
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I posted a blog on the SAP site here that discussed the implications of mobile clients. I want to re-emphasize the issue as it is crucial.

While at Greenplum we routinely replaced older EDW platforms and provided stunning performance. I recall one customer in particular where we were given a query that ran in 7 hours and Greenplum executed the query in seven seconds. This was exceptional… more typical were cases where we reduced run-times from several hours to under 30 minutes… to 10 minutes… to 5 minutes. I’m sure that every major competitor: Teradata, Greenplum, Netezza, and Exadata has similar stories to tell.

But 5 minutes will not cut it if you are servicing a mobile client where sub-second response to the device is a requirement… and 10 minutes is out of the question. It does not matter if it ran in 10 hours before… 10 minute response is not acceptable to a mobile device.

Today we see sub-second response delivered to our phones by custom applications built on special high-performance platforms designed specifically to service a mobile client: iPhones, iPads, and Android devices.

But what will we do about the BI applications built on commercial platforms which have just used every trick in the book to become one of the 5 minute stories mentioned above?

I think that there are only a couple of architectural choices.

  1. We can rewrite the high-value queries as custom applications using specialized infrastructure… at great expense… and leaving the vast majority of queries un-serviced.
  2. We can apply the 80/20 rule to get the easiest queries serviced with only 20% of the effort. But according to Murphy the 20% left will be the highest value queries.
  3. We can tack on expensive, specialized, accelerators to some queries… to those that can be accelerated… but again we leave too much behind.
  4. Or we can move to a general purpose high performance computing platform that can service the existing BI workload with sub-second response.

In-memory computing will play a role… Exalytics provides option #3… HANA option #4.

SSD devices may play a role… but the performance improvements being quoted by vendors who use SSD as a block I/O device is 10X or less. A 10X improvement applied to a query that was just improved to 10 minutes yields a 1 minute query… still not the expected level of service.

IT departments will have to evaluate the price/performance, not just the price, as they consider their next platform purchases. The definition of adequate response is changing… and the old adequate, at the least cost, may not cut it. Mobile clients are here to stay. The productivity gains expected from these devices is significant. High performance BI computing is going to be a requirement.

Cloud Computing and Data Warehousing: Part 1 – The Architectural Issues

My apologies… I was playing with the iPad version of WordPress and accidentally published a very rough outline/first draft of this post. I immediately un-published it… but not before subscribers were notified that there was a new post.

I wonder about the idea that data warehousing is suited to operate in the cloud? This was prompted by Paraccel‘s venture to deploy on the Amazon EC2 cloud infrastructure. Lets work through the architectural implications…

Here are the assumptions I’ll take into this exploration:

  1. A shared-nothing architecture is required to scale.
  2. Cloud infrastructure is cost-effective when the infrastructure is under-utilized and workloads can be consolidated to achieve full utilization… and not so cost-effective when the infrastructure is highly utilized. This is because applications can easily share underutilized resources in the Cloud.
  3. Cloud infrastructure is justified when the workload is inconsistent and either CPU or storage requirements fluctuate widely over the business cycle. This is because a Cloud is elastic and can easily flex as the requirements fluctuate. Cloud computing may not be well suited to static workload requirements.

You can probably see where I’m going with this from the assumptions.

In the end I’ll suggest that there is a database architecture that is suited to warehousing and cloud computing… but let me build to that.

Before I start let me also be clear that I am talking about the database infrastructure… not the application/BI infrastructure required for data warehousing. The BI and ETL components are perfectly suited to cloud computing… they reflect a workload that, in general, runs on under-utilized hardware with BI running during the day and ETL running at night. I have suggested this to my current employer… but alas, I am neither King nor a member of Court.

So in Part 1 let me discuss my first two assumptions and the implications… In Part 2 I’ll discuss data warehousing and elasticity… In Part 3 I’ll consider the Paraccel/Amazon collaboration and in Part 4 I’ll wrap up and consider several new things coming that may change the equations.
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I’ll not work too hard to justify my first assumption… I think that it is well-understood that a shared-nothing architecture provides the best possible approach to scale out. Google and others use this approach to scale to hundreds of petabytes of data and Teradata, Greenplum, Netezza, Paraccel, SAP HANA, and others use it in the data warehouse space. Exadata uses a hybrid approach that scales I/O in a shared-nothing-like storage subsystem… but fails to scale as it passes data to the RAC layer (see Kevin Closson here on the subject).

But the implications are significant for our cloud discussion. First, cloud infrastructure is designed to support general client-server or web-server based commercial computing requirements. A shared-nothing database cluster is a specialized infrastructure optimized for database processing. Implementing the specialized problem on the generalized infrastructure is possible, but sub-optimal. Next, cloud computing requires, more or less, a shared storage subsystem. A shared-nothing architecture shares nothing. Implementing a shared-nothing database on a shared storage subsystem is possible, but sub-optimal.

I believe that the second assumption is also pretty straightforward. The primary rationale for cloud computing comes from the recognition that many data centers deployed applications on servers that were not fully utilized. By virtualizing the hardware on a cloud platform the data center could better service the applications with fewer hardware resources and therefore less cost.

So… in order for cloud computing to be a perfect fit we need to observe a data warehouse database workload with underutilized hardware infrastructure… You might ask yourself… are there underutilized hardware resources upon which my EDW is built? In most cases I believe that the answer to this question will be “no”. Almost every EDW I’ve seen is over-burdened… stretched… with users demanding more and more resource… more data, more users, more queries, deeper queries drive the resource requirements up exponentially. The database is swamped all day with queries and swamped all night by ETL and reporting tasks.

So let’s end this blog concluding that there is a problematic architectural mismatch between a shared cloud and a shared-nothing implementation… and that if your warehouse database platform is highly utilized then there may be little benefit from implementing a warehouse in the cloud.

See Part 2 here