Tag: Amazon

More thinking on Specialized Databases

Recently I posted (here) some thinking that suggested that the cost of replicating data into specialized databases might outweigh the benefits of specialization. This post will present a counter view and try to sort out when a specialized database might make sense.

In the ZDNet post here: “Look at What Google and Amazon are doing with Databases: That’s your future” Toby Wolpe and Neo Technology CEO Emil Eifrem suggest that:

“The era of the one-size-fits-all database is over. It used to be when I grew up as a developer that for the architect in the project, when it came to choosing the bottom layer of the stack — the persistence layer — the choice was Microsoft, or IBM, or Oracle, or Sybase. It was a vendor choice.

They were all the same type of database. But that era has gone forever and it will never come back because data is just so big and so irregularly shaped now that you’re always going to be able to get a hundred times improvement, a thousand times improvement, a million times improvement if you get a data technology that is shaped like the shape of your data.”

While I have suggested that a swiss army knife DBMS that solves many problems from a single data source… thereby eliminating the cost and complexity of data replication and data synchronization… might provide a sensible choice for most commercial applications.

Actually I agree with Eifrem and Wolpe in many respects… but there is a difference in our starting assumptions. Let me be clear first about where I strongly agree.

When data volumes grow to web-scale… to Google-scale or Amazon-scale… then the inefficiencies of one-size-fits-all amplify and become intractable… so with a specialized DBMS you might indeed see 100X, 1000X, or more performance advantage and gain a competitive edge from replication and specialization.

But a lot of core data is not Big Data. This is where we do not seem to agree. While our company’s all aspire to have a customer database that is in the petabyte range… it is just not usually the case. Likewise we aspire to have a transaction database requiring petabyte scale… but it just is not the case in most businesses even if you keep years and years of history.

Let’s consider graph databases… Maybe customer data should be in a graph database to specialize it for processing relationships. But this is likely to make it sub-optimal for many other processes… in fact it is the thesis of the ZDNet article that it will make it sub-optimal for many other processes… and so replication to more specialized databases is the only alternative.

How might we handle this relationship problem in a generalized DBMS? HANA, for example, can form graphs in-memory from data shaped into columns… unfolding the graphDB blade from the swiss army knife when required but storing the data in a generalized shape otherwise.

It may be true that there could be orders of magnitude advantage for big data shaped into a specialized graphDB form… But if your customer database is in the terabyte range or less, then the advantage may be negligible… or at least the advantage may not justify the cost of replication into two forms.

And think about the implications of specializing big data. Google replicates tens of petabytes of data into multiple shapes to gain competitive advantage… and ten petabytes specialized and replicated ten times is really really big data.

So I agree with parts of the ZDNet post… big data companies are likely to be pushed by the competition to store the data multiple times in specialized replicated big databases… and for this you will look to Google, Amazon, Netflix, and the like for database technology. But most enterprises will be able to store core data in generalized databases… and will extend into big data realms only as machine-to-machine transactions and/or the Internet of Things drive them there… and then they will extend their data architectures rather than replicate again and again.

Thoughts on AWS Redshift…

English: In visible light, 4C 71.07 is less than impressive, just a distant speck of light. It’s in radio and in X-rays – and now, gamma rays – that this object really shines. (Photo credit: Wikipedia)

The shared-nothing architecture has, from the beginning, offered the promise of using hardware to solve performance problems rather than applying staff and tuning. By this I mean… if you can add nodes and scale out to improve query response then why not throw hardware at performance problems rather than build a fragile infrastructure of aggregate tables, cubes, pre-joined/de-normalized marts, materialized views, indexes, etc. Each of these performance workarounds are both expensive to build and expensive to operate.

There are several reasons, I think tuning has been more popular than scaling. Not in any particular order:

First, hardware vendors made it too hard to order/provision new nodes. You could not just press a button and buy capacity. Vendors wanted to charge you for terabytes when all you wanted might be CPU and Memory to fix the problem (see here, sigh). You had to negotiate a deal with a rep, work through your procurement group, wait weeks for delivery. Then, the hardware you have might not match the hardware for sale. New models could not be mixed with old nodes… so you had to consider a whole new cluster. The process was so not-agile. There have been attempts to fix this… and some of them are credible… but none are popular.

Next, the process to install the new nodes was moderately difficult… not rocket science but not seamless to be sure. Data had to move. Backups had to be reconfigured and sometimes old backups could not be easily restored to the new configuration. There was no easy way to burn in the new hardware and if it failed early there were issues reversing the process. It just was not considered an everyday operational process… it was the exception and that made it tough. This process too has improved over time but it never became a no-brainer.

Finally, buying hardware is a capital expense (CAPEX). Even if you had to pay more in people costs to do the hard work of tuning those were operational expenses… and funding was easier to get.

Redshift changes the game here. Even if the Paraccel database is just OK (see here)… and if the overhead of running in the virtualized AWS environment makes it worse… it is still OK. You can provision new hardware in a couple of minutes. If Teradata is 25% faster than Paraccel for your query set… so what? You can add 25% more Redshift for a fraction of the extra cost of Teradata. Need more performance? Dial it in. Need permission? No problem because it is all OPEX dollars.

Redshift will deliver the flexibility to make scale out less expensive than tune it out. The TCO reductions from running a simple system where hardware solves performance problems instead of ETL and staff will be significant. This is how it always should have been.

The issue for Redshift will be… given the trend to reduce the data latency from operations to BI… can you move significant amounts of data from on-premise into the cloud fast enough to meet service level agreements?

Do not overlook Redshift… Amazon could be a player in the EDW space… But look for other databases to make inroads here as well. In-memory databases could work well in the cloud as they avoid some of the hardware abstraction required to access disks.

Related articles

My 4 Cents: Vertica and Paraccel 1Q2013

(Photo credit: Wikipedia)

Summary

Vertica is the product I saw the most. In fact before they were acquired they were beginning to pop up a lot. The product is innovative in several dimensions. It is wholly column-oriented with several advanced columnar optimizations. Vertica has an advanced data loading strategy that quickly commits data and then creates the column orientation in the background. This greatly reduces the load time but slows queries while the tuples are transformed from rows to columns. Vertica offers a physical construct called a projection that may be built to greatly speed up query performance. Further, they provide a very sophisticated design workbench that will automatically generate projections.

Paraccel is the company I saw the least. I never saw it win… but I know that it does, in fact, win here and there. My impression, and I use this fuzzy word intentionally as I just don’t know, is that Paraccel is a solid product but it does not possess any fundamental architectural advantages that would allow it to win big. Every product will find an acorn now and again. The question is whether in a POC with the full array of competitors there is a large enough sweet spot to be commercially successful? I think that Paraccel cannot win consistently against the full array. Paraccel is now the basis for the Amazon Redshift data warehouse as a service offering. This will keep the product in the game for a while even if the revenues from a subscription model do not help the business much.

Where They Win

Vertica wins when projections are used for most queries. They are not likely to win without projections. This makes them a very effective platform for a single application data mart with a few queries that require fast performance… or for a data mart where the users tend to submit queries that fit into a small number of projection “grooves”.

Paraccel wins in the cloud based on Redshift. They can win based on price. They win now and again when the problem hits their sweet spot. They win when they compete against a small number of vendors (which increases their sweet spot).

Where They Lose

Vertica loses in data warehouse applications where queries cut across the data in so many ways that you cannot build enough projections. Remember that projections are physical constructs with redundant data.

Paraccel loses on application-specific marts and other data marts when the problems fit either Vertica’s projections or Netezza’s zone maps. They lose data warehouse deals to both Teradata and Greenplum when the query set is very broad. They will lose in Redshift when performance is the key… maybe. I have always thought that shared-nothing vendors made it too hard and too expensive to scale out. It should always have been easier to add hardware to improve performance than to apply people to tuning… but this has not been the case… maybe now it is (see here)?

In the Market

Since the HP acquisition the number of times Vertica shows up as a competitor has actually dropped. I cannot explain this but HP has had a difficult time becoming a player in the data warehouse space and had several false starts (Neoview, Exadata, …). The product is sound and I hope that HP figures this out… but HP is primarily a server vendor and it will be difficult for them to sell Vertica and stay agnostic enough to also sell HANA, Oracle, Greenplum, and others.

The Amazon Redshift deal breathes life into Paraccel. They have to hope that the exposure provided by Amazon will turn into on-premise business for them. They are still a venture-funded small company who has to compete against bigger players with larger sales forces. It will be tough.

My Guess at the Future

I worry about Vertica in the long run.

Until the Amazon deal I would have guessed that Paraccel was done… again, not because their technology was bad… it is not… but because it was not good enough to create a company that could go public and there was no apparent buyer… no exit. The Amazon Redshift deal may provide an exit. We will see? Maybe Amazon can take this solid technology into the cloud and make it a winner?

Cloud Computing and Data Warehousing: Part 3 – ParAccel on EC2

In the previous post here I suggested that a SAN-based, cloudy, EDW is about 4X the cost for the same performance over a data warehouse appliance.. and I described why. I have actually seen this comparison.

It is difficult to compare Amazon EC2 hardware to the hardware typically assembled in a shared-nothing EDW cluster whether the hardware is from HP, Dell, Sun, IBM, or Teradata. So let’s assume that Amazon gets a 20% edge due to huge volume purchases over your firm. Note that this is a significant edge since the hardware is a commodity. Further, lets assume that Amazon gets another 30% edge in TCO on system administration costs. This is the cost of staff to manage the Linux OS and the hardware components. This may also be generous to the Amazon side of the equation. The numbers are not important… you can put in whatever seems to model your situation best… if you work for a large efficient company the numbers may go down for EC2.

Lets also assume that you reserve and receive dedicated hardware on EC2. This will not be the case but lets continue to build a best-case scenario for EC2.

From these numbers we can assume that the EC2 configuration will be 3X the cost for the same performance as a dedicated purpose-built database cluster. Again this assumes that the EC2 hardware is dedicated so this number is optimistic.

So why would anyone do this? Because EC2 has no up-front capital expense associated… it is an operating expense. This is significant.

So what is the advantage of buying ParAccel on EC2? I’m unsure. ParAccel has not done particularly well in the marketplace… but it is not clear that this is a technology issue. The answer could lie in the fact that companies deploy ParAccel on EC2 for data mart or application-specific workloads that may not use 100% of the hardware resources provided?

I think that if you work through these three blogs you can get an idea of how to model the opportunity for yourself. If the ability to spend OPEX dollars with Amazon is important… even if you need 3X the hardware… then this is a very interesting way to go.

But do not imagine that you are getting the same performance with ParAccel on EC2 that you wold get with ParAccel on HP or Dell… for a fraction of the price. There is no architectural advantage in ParAccel on EC2 over Vertica or Greenplum or any other DBMS that can run on EC2… ParAccel is, however, trying something new and interesting… if you understand the trade-offs.

In the last blog of this series (here) I’ll discuss some new approaches that may change the game… including another interesting possibility for ParAccel going forward.

Cloud Computing and Data Warehousing: Part 2 – An Elastic Data Warehouse

In Part 1 of this topic (here) I suggested that cloud computing has the ability to be elastic… to expand and maybe contract the infrastructure as CPU, memory, or storage requirements change. I also suggested that the workload on an EDW is intense and static to point out that there was no significant advantage to consolidating non-database workloads onto an over utilized EDW platform.

But EDW workload does flex some with the business cycle… quarter end reporting is additive to the regular daily workload. So maybe an elastic stretch to add resources and then a contraction has value? It most probably does add value.

The reason shared-nothing works is because it builds on a sharded model that splits the data across nodes and lets the CPU and I/O bandwidth scale together. This is very important… the limiting factor in these days of multi-core CPUs is I/O bandwidth and many nodes plus shards provides the aggregate I/O bandwidth of all disk controllers in the cluster.

What does that mean with regards to building an elastic data warehouse? It means that with each elastic stretch the data has to be re-deployed across the new number of shards. And because the data to be moved is embedded in blocks it means that the entire warehouse, every block, has to be scanned and re-written. This is an expensive undertaking on disk… one that bottlenecks at the disk controller and one that bottlenecks worse if there are fewer controllers (for example in in a SAN environment). Then, when the configuration is to shrink it process is repeated. In reality the cost of th I/Oe resources to expand and contract does not justify the benefit.

So… we conclude that while it is technically possible to build an elastic EDW it is not really optimal. In every case it is feasible to build a cloud-based EDW… it is possible to deploy a shared-nothing architecture, possible to consolidate workloads, and possible to expand and contract… but it is sub-optimal.

The real measure of this is that in no case would a cloud-based EDW proof-of-concept win business over a stand-alone cluster. The price of the cloudy EDW would be 2X for 1/2 the performance… and it is unlikely that the savings associated with cloud computing could make up this difference (the price of SAN is 2X that of JBOD and the aggregate I/O bandwidth is 1/2… for the same number of servers… hence the rough estimates). This is why EMC offers a Data Computing Appliance without a SAN. Further, this 4X advantage assumes that 100% of the SAN-based cluster is dedicated to the EDW. If 50% of the cluster is shared with some other workload then the performance drops by that 50%.

In the next post (here) I’ll consider Paraccel on the Amazon Cloud…

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…

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