My 2 Cents: Oracle Exadata 1Q2013

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Since my blogs tend to be in response to some stimulus they may not reflect a holistic view on any particular product. The “My 2 Cents” series will try to provide a broader view…

To help pay the bills please consider this as you read on…


OK, I hate Oracle marketing (see here and here). They are happy to skirt the edge of the credible too often. But let’s be real… Exadata was a very smart move… even if it a flawed product. The flaws are painful but not fatal… and Oracle can now play in the data warehouse space in places they could not play before. I do not believe that Exadata is a strong competitor as you will see below… it will not win many “fair” POCs… but the fight will be more than close enough to make customers with existing Oracle warehouses pick Exadata once they consider the cost of migration. This is tough… it means that customers are locked in to a relatively weak alternative… and every Oracle customer (and every Teradata customer and every SQL Server customer and every DB2 customer) should consider the long-term costs of vendor lock-in. But each customer has to weigh this for themselves… and this evaluation of the cost of lock-in is about neither architecture nor marketing…

Where They Win

First and foremost Exadata wins when there is an existing data warehouse or data mart on Oracle that will have to be migrated. My recommendation to customers is that they think about this carefully before they engage other vendors. It is a waste of everybody’s time to consider alternatives when in the end no alternative has a chance… and it is a double waste to do a POC when even a big technical win by a competitor cannot win them the business.

Exadata can win technically when the data “working set” is small. This allows Exadata to keep the hot data in SSD and in memory and better still, in the RAC layer. This allows Oracle to win POCs where that can suggest a subset of the EDW data is all that is required.

Exadata can win when the queries required, or tested, contain highly selective predicates that can be pushed down in the first steps of the explain plan. Conversely, Exadata bonks when lots of data must be pulled to the RAC layer to perform a join step.

Where They Lose

Everyone who has an Exadata system or who is considering one should view the two videos here. The architectural issues are apparent… and you can then consider the impact for your workload.

As noted above… in an Exadata execution plan the early simple table scans and projection are executed in the storage layer… subsequent steps occur in the RAC layer… if lots of data has to be moved up then the cluster chokes.

There are times when the architectural limitations are just too large and a migration is required to meet the response time requirements for the business. This often happens when Exadata is to support a single application rather than a data warehouse workload… In other words, if the cost of migrating away from Oracle is small, either because the applications to be moved are small or because an automated tool is available to mitigate the cosy or because the migration costs are subsidized by another source, then Exadata can lose even when there is a migration required.

Exadata can be beat on price… unless you count the cost of migration.

In the Market

For the reasons above, Exadata wins for current Oracle customers. There was a honeymoon when Exadata was winning some greenfield deals against other competitors… but these are now more rare.

My Guess at the Future

I think that the basic architecture of Exadata is defensible… having a split configuration is , after all, not completely foreign. Teradata and Greenplum and others use master nodes split from data nodes… and this is where is I predict we’ll see Oracle go. Over time, more execution steps will move to the storage layer and out of the RAC layer and in the end, Exadata will look ever more like a shared-nothing implementation. This just has to be the architectural way forward for Exadata (but don’t expect LE to stand up anytime soon and admit that he was wrong all of these years about the value of a shared-nothing architecture).

Phil has alerted us that there will be some OLTP/BI enhancements coming (see the comments section here)… which stole away a prediction I would have made otherwise.

The bottlenecks pointed out by Kevin Closson (as above and more here) need to be addressed… but to some extent these issues are the result of hardware constraints… and the combination of better hardware configurations and the push-down of more execution steps can mitigate many of the issues.

It will be a while before the Exadata architecture evolves to a point where the product is more competitive… and from now to then I think the World will be as I described it above… Oracle zealots will pick Exadata either as a religious stance or to avoid the cost of a migration… others will mostly go elsewhere…

Coming next… my 2 Cents on Netezza…

My .2 Cents on HANA

Out of respect for my employer let me repeat what I have said before regarding HANA… HANA provides far and away the best price/performance of any data warehouse… no question. HANA will win the performance portion of every POC just based on the facts detailed in this graphic:

Latency 3

Other products database code lives thousands to tens of thousands times further away from the data than in HANA. Based on this I honestly believe that HANA will come to dominate the market… And I’ll say more on this when I post my 2 cents on HANA…

But Teradata is the leader for good reason… and in the next posts providing my 2 cents I will find good things to say about several other  products…

This personal blog is not for marketing any particular product… it is about architecture…

To their credit my employer has given me no grief over the Teradata post… but I know that some folks are wincing over the idea that I would say something good about the competition… My apologies…

My 2 Cents: Teradata 1Q2013

Since my blogs tend to be in response to some stimulus they may not reflect a holistic view on any particular product. The “My 2 Cents” series will try to provide a broader view…

Teradata Storage Rack
Teradata Storage Rack (Photo credit: pchow98)


Despite my criticisms of some of their market positions (here, here, here, and here) Teradata provides the single best data warehouse platform in the market, hands-down. As an EDW, or data mart it is the best. It will be very competitive as an analytics mart and/or as an operational data store. It has a very complete eco-system of utilities and offers a robust set of Reliability, Availability, Serviceability, and Recoverability (RASR) features to make the eco-system solid. Performance is very good… Teradata should win more POCs than they lose… and they have become more competitive on price… so their price/performance is good if not great.

I recommend a POC for most customers in most cases… you can often save 20%-30% in a competitive situation.. but if you don’t have any special requirements… if you are building a standard BI/DW eco-system then Teradata would be the only vendor I would trust without a POC.

Where They Win

Now that they support columnar tables and columnar projection Teradata should win way more POCs than they lose (before columnar support they could lose to the column stores or to hybrids like Greenplum). The Teradata optimizer is very robust. It efficiently solves for a broad array of queries, and for a mixed workload that cuts across the data is many ways. This makes Teradata well-suited as the platform for an EDW.

Every RDBMS has a sweet spot where they win… so Teradata will not win every POC. But if you POC for an EDW and you prove with a full contingent of data, with queries that cut across the data in several ways, with a fair emulation of data loading, querying, loading , and querying… with a full workload… Teradata is tough to beat.

Where They Lose

The shared-nothing architecture is an imperfect fit on a single node… so other players can win smaller data warehouses that can fit on 1-2 nodes. In addition, they can be beat for very large configurations (1PB and above…) by Hadoop.

Teradata can be beat when the workload consists of very complex queries and/or where the problem to be solved requires fantastic response on a small number of CPU-intensive queries… this is a side-effect of spooling the intermediate results to a block device.

Teradata can be beat when data is trickled in at a high, continuous, rate.

Teradata can be beat when a query set goes through the data in a narrow way, using a single index or the equivalent, as might be the case for a data mart.

Teradata can be beat on price.

In the Market

For the reasons above, Teradata is the leader in the DW platform market. Recent competition from Exadata, Netezza, Greenplum, Vertica… and now HANA… has cut margins but not impacted business growth too much. Competitors have projected Teradata’s demise for 20 years now… but the product continues to set the standard.

As noted here, I believe that Hadoop will squeeze Teradata at the 1PB level and above…

My Guess at the Future

Teradata has three architectural challenges to address… and I suspect they will manage all three more-or-less.

First, the old architecture which was designed for very small DRAM configurations forces unnecessary I/O in violation of Gray and Putzolu’s Five Minute Rule (see here). This will be mitigated in the short-term by writing spool to SSD devices… and in the medium term by writing spool to NVRAM. If these mitigations are not sufficient then Teradata may have to consider re-engineering in a data flow scheme… but this will be tough.

Next, there are several advances in network technology coming in the next 2-3 years… and software defined networks will impact the space as well. ByNet may have served its purpose… providing Teradata with a significant edge for 20+ years… but Teradata may consider moving to an off-the-shelf network (see here).

Finally, a truly active data warehouse requires support for simultaneous OLTP and BI workloads… I would expect Teradata to build in the sort of hybrid OLTP/BI table capability now supported by both Vertica and HANA… and quasi-supported by Gemfire/Greenplum.

Teradata has some interesting business challenges as their margins shrink… and one of those challenges is that their expensive 3-person relationship/technical/industry sales team approach will face some pressure. But it is these sales teams that also provide Teradata an edge. They are the only databases vendor who can field team after team of veterans who understand both the technology and the vertical space.

If I were King of Teradata I might try to push downstream and build a configuration optimized for the low end. This would not be a high-margin hardware business but it would sell services and increase market share.

Getting started with Hadoop… Enhance Your Data Warehouse Eco-system

Gartner thinks that the Big Data hype is going to die down a little for the lack of progress… (see here) Companies without web-scale, big, data are finding it hard to do anything commercially interesting… still CIO’s sense that Hadoop is going to become important. This post provides a suggestion that might help you to get started.

Hadoop goes here

In most data warehouse eco-systems there is an area, a staging place, where data lands after it is extracted from the source and before it is transformed. Sometimes the staging area and the ETL process are continuous and data flows through the ETL hardware system without seeming to land… but it usually is written somewhere.

The fact is that often enterprises only move data to their data warehouse that will be consumed by a user query. Often users want to see only lightly aggregated data in which case aggregation is part of the ETL process… the raw detail is lost. A great example of this comes from the telecommunications space. Call details may be aggregated into a call record… and often call records are sufficient to support a telco’s business processes.

But sometimes the detail is important. In this case the staging area needs to become a raw data warehouse… a place where piles of data may be stored inexpensively for a time… possibly for a long time.

This is where Hadoop comes in. Hadoop uses inexpensive hardware and very inexpensive software. It can become your staging area and your raw data warehouse with little effort. In subsequent phases, you can build up a library of the jobs that need to look at raw data. You might even start to build up a series of transformations and aggregations that might eventually replace your ETL system.

This is what Sears Holdings is up to (see here).

As I suggested in an earlier post, the economics of Hadoop make it the likely repository for big data. Using Hadoop as the staging area for your data warehouse data might provide a low risk way to get started with Hadoop… with an ROI… preparing your staff for other Hadoop things to come…


A Look Back at 2012

There seems to be a sort of odd tradition for bloggers to look back at the past year as the New Year starts to unfold. Here is my review of my posts and some presents

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Top Post

Far and away the most viewed post was Exalytics vs. HANA What are they thinking? This simply notes that these two products are not really comparable sharing only the descriptor “in-memory”.

My Favorite Post

I liked this the best… ’nuff said: What is Big Data?

OK, here is my 2nd favorite: A Quick Five Minute Rule Update for In-memory Databases, but you probably need to read the prequel first: The Five Minute Rule and In-memory Databases

These papers and the underlying thinking by smarter folks than I will inform you about the definition of Hot Data from the point of pure IT economics.

The Most Under-rated Post

This is the post I thought was the most important… as it might strongly influence data warehouse platform buying decisions over the next few years… And it might even influence the stocks you pick: The Future of Hadoop and Big Data DBMSs

Some Other Posts to Read

Here are two posts that informed me:

The Five Minute Rule… This will point you to a Wikipedia article that will point you to the whole series of papers.

What Every Programmer Should Know About Memory… This paper goes into gory detail about how memory works inside a processor. It is hardware-centric for you software folks… but provides the basis for understanding why in-memory DBMSs are fast and why Exadata is not an in-memory DBMS.

And some other Good Stuff

Kevin Closson on Exadata

Google Research

Thank you for your attention last year. I hope that each of you has a safe, prosperous, and happy new year…

– Rob

Teradata’s HANA Mathematics

I recently pointed out some silliness published by Teradata to several SAP prospects. There is more nonsense that was sent and I’d like to take a moment to clear up these additional claims.

In their note to HANA prospects they used the following numbers from the paper SAP published here:

# of Query Streams


10 20


# of Queries per Hour (Throughput)


36,600 48,770


Teradata makes several claims from these numbers. First they claim that the numbers demonstrate a bottleneck that is tied to either the NUMA effect or to the SMP Knee Curve. This nonsense is the subject of a previous blog here.

For any database system as you increase the number of queries to the point where there is contention the throughput decreases. This is just common sense. If you have 10 cores and 10 threads and there is no contention then all threads run at the same speed as fast as possible. If you add an 11th thread then throughput falls off, as one thread has to wait for a core. As you add more threads the throughput falls further until the system is saturated and throughput flattens. Figure 1 is an example of the saturation curve you would expect from any system as the throughput flattens.

There are some funny twists to this, though. If you are an IMDB then each query can use 100% of a core. If you are multi-threaded IMDB then each query can use 100% of all cores.  If you are a disk-based system then you give up the CPU to another query while you wait for I/O… so throughput falls. I’ll address these twists in a separate blog… but you will see a hint at the issue here.

Teradata claims that these numbers reflect a scaling issue. This is a very strange claim. Teradata tests scaling by adding hardware, data, and queries in equal amounts to see if the query performance holds constant… or they add hardware and data to look for a correlation between the number of nodes and query performance… hoping that as the nodes increase the response time decreases.  In fact Teradata scales well… as does HANA… But the hardware is constant in the HANA benchmark so there is no view into scaling at all. Let me emphasis this… you cannot say anything about scaling from the numbers above.

Teradata claims that they can extrapolate the saturation point for the system… this represents very bad mathematics. They take the four data points in the table and create an S curve like the one in Figure 1… except they invert it to show how throughput decreases as you move towards the saturation point… Figure 2 shows the problem.

If you draw a straight line through the curve using any sort of math you miss the long tail at the end. This is an approximation of the picture Teradata drew… but even in their picture you can see a tail forming… which they ignore. It is also questionable math to extrapolate from only four observations. The bottom line is that you cannot extrapolate the saturation point from these four numbers… you just don’t know how far out the tail will run unless you measure it.

To prove this is nonsense you just have to look here. It turns out that SAP publicly published these benchmark results in two separate papers and this second one has numbers out to 60 streams. Unsurprisingly at 60 streams HANA processed 112,602 queries per hour while Teradata told their customers that it would saturate well short of that… at 49,601 queries (they predicted that HANA would thrash and the number of queries/hour would fall back… more FUD).

Teradata is sending propaganda to their prospects with scary extrapolations and pronouncements of architectural bottlenecks in HANA. The mathematics behind their numbers is weak and their incorrect use of deep architectural terms demonstrates ignorance of the concepts.  They are trying to create Fear, Uncertainty, and Doubt. Bad marketing… not architecture, methinks.

The Future of Hadoop and of Big Data DBMSs

Image representing Hadoop as depicted in Crunc...

About four years ago Michael McIntire and I were pondering the rise of Hadoop. This blog will share bits of that conversation, provide an update based on the state of Hadoop today, and suggest a future state…

Briefly… we believed that the Hadoop eco-system was building all of the piece-parts of a very large database management system. You could see the basics: a distributed file system in HDFS, a low-level query engine in Map/Reduce with an abstraction in Pig, and the beginnings of optimization, SQL, availability, backup & recovery, etc.

We wondered why this process was underway… why would enterprises go to Hadoop when there were perfectly good relational VLDBs that could solve most of the problems… and where they could not… extending a mature RDBMS would be easier than the giant start-from-scratch of Hadoop.

We saw two reasons for the Hadoop project, process, and progress:

  • Michael pointed out that the RDBMS vendors just did not understand how to price their products on “Big Data” (to be fair that term was not in use then)… if you have 7PB of data, as Michael did… then at the current $35K/TB list price the bill would be $245M. Even if you discounted to $1K/TB the tab would be $7M. The DBMS vendors were giving the big guys a financial incentive to Build instead of Buy… and so Google Built and Yahoo Built and Hadoop emerged.
  • I pointed out that the academic community would support this… the ability to write a thesis based on new work in the DBMS space was becoming harder… but it was possible to sponsor papers that applied DBMS concepts to Hadoop and keep the PhD pipeline filled.

So there was funding, research, and development.

The narrative from here on is my own… Michael is off the hook..

Today Hadoop has a first release in the public domain. Dozens of companies are working to extend the core… some as contributors, some with a commercial interest, many with both incentives. The stack is maturing… and we now easily imagine a day when Hadoop will rival Teradata, Exadata, Netezza, and Greenplum in VLDB performance… with some product maturity and a rich set of features. And if Hadoop gets close and the price is free (or nearly so…) then the price/performance of Hadoop will make it unbeatable for “Big Data”.

In fact, the trigger for writing this piece now was the news a few weeks back from one of our Hadoop partners that HIVE was POC’d against one of the databases mentioned above on a big data problem and came close. The main query ran in 35 minutes on the DBMS and in 45 minutes with HIVE. The end is in sight… and sooner than expected.

What might this mean for the future?

Imagine a market where Hadoop can solve for big data problems… let’s say problems over 500TB just to draw a line… with the same performance as the best RDBMS, in a write-once/read-many use case like a data warehouse… for free. For FREE… plus the cost of the hardware. Hadoop wins… no contest.

Let’s suggest a market from 50TB to 500TB where a conventional RDBMS can out-perform Hadoop by 2X more-or-less… but Hadoop is free… so only applications where the performance matters can pay the price premium.

And let’s suggest a high performance in-memory database (IMDB) market that beats disk-based and SSD-based RDBMS by 50X for a 50% premium (based on new technologies like phase-change memory see here…) and can beat Hadoop by 1000X but at a higher cost.

You can see the squeeze:

  • IMDB will own the high performance market… most-likely in the 100TB and under space…
  • Hadoop will own the big data 500TB+ low-cost market…
  • and the conventional DBMS vendors will fight it out for adequate-performance/medium-priced applications from 100TB to 500TB… with continued pressure from the top and the bottom.

Economics will drive this. The conventional DBMS vendors are moving to SSD’s… which increases their price in the direction of an IMDB… and increases their price/performance in the same good direction. But the same memory in SSD’s will soon be generally available as primary memory. So the IMDB prices and the conventional DBMS prices will converge… but the IMDB products will retain a 50X-100X performance advantage by managing the new memory as memory instead of as a peripheral device. Hadoop may or may not leverage SSD’s… but it will be free.

Squeezed, methinks…

Commercial Post Update: HANA and Exalytics and Teradata and IMDB Economics

English: Hawaiian spear fisherman near Hana; M...
English: Hawaiian spear fisherman near Hana; Maui, Hawai‘i. ca. 1890. (Photo credit: Wikipedia)

Here are links to several commercial posts on the Experience HANA Blog FYI…

The Five Minute Rule and HANA: This is a rehash of my posts here applying the famous Five Minute Rule to in-memory databases.

HANA & Exalytics: There is Barely Any Comparison: This is a rehash of my post here pointing out that Exalytics and HANA do not really compete.

HANA vs. Teradata – Part 1: This is a response to some poor thinking posted by Teradata. There is some new content that could be worth a look.

HANA vs. Teradata – Part 2: This continues the response… but it is a rehash of the post here on the rational economics of in-memory databases. Frankly, I had just reread the Teradata posts and wrote this while still annoyed… as a result it is a little flip and despite the junk posted by Teradata I might have shown them a little more respect…

Exalytics vs. Exadata: This post suggests some oddness in Oracle’s positioning of Exalytics and Exadata… maybe worth a look.

The Five Minute Rule and In-memory Databases

I was recently reminded of a couple of papers written by Jim Gray and Gianfranco Putzolu  that calculated the cost of keeping data in memory vs the cost of paging it in from disk. I was happy to see that the thread was being kept alive by Goetz Graefe.

These papers used the cost of each media to determine how “hot” data needed to be to be cost-effectively stored in-memory. The 1987  five minute rule (click here to reference the original papers) was so named because at that time and based on the relative costs of CPU, Memory, and Disk; a 1KB  record that was accessed every five minutes could be effectively stored in memory and a 4KB block of data broke-even at two minutes.

In 2009, with CPU prices coming down but the number of instructions executed per second going up, and with memory and prices down, the break-even point between keeping 4KB in memory or on a SATA disk was 90 minutes.

Let’s be clear about what this means. Based solely on the cost of CPUs, RAM, and SATA drives; any data that is accessed more frequently than each 90 minutes should be kept in memory. This does not include any ROI based on the business benefits of a speedy response. It does not adjust for data compression which allows more than 4KB of user data to use 4KB of RAM. Just pure IT economics gets us to this point.

So… if you have data in a data warehouse or a mart that is touched by a query at least once every 90 minutes… it is wasteful to store it on disk. If you have an in-memory database than can compress the data 2X and use it in its compressed form, then the duration goes up to 180 minutes. You do not have to look any further than this to find the ROI for an in-memory data base (IMDB).


Chaos, Cloud Computing, and the Data Warehouse


David Linthicum suggests here that Shadow IT is not all a bad thing. He references a PricewaterhouseCoopers study that suggests that 30% of all IT spending comes from the business directly… from outside of the IT budget.

In the data warehouse space we can confirm these numbers easily. Just google on “data mart consolidation” to see the impact of the business building their own BI infrastructure in order to get around the time-consuming strictures and bureaucratic processes that IT imposes on a classic EDW platform. Readers… think of the term “data governance”… governance implies bureaucracy. And a “single version of the truth” implies a monopoly (governed by IT). We need a market for ideas to support our business intelligence… and a market is a little chaotic.

What we need is a place where IT says to the business… we cannot get you integrated into our formal EDW infrastructure as fast as you would like… but don’t go and build your own warehouse/mart on your own shadow platform. Let us provide you with a mart in the cloud. Take the data you need from our EDW. Enhance it as you see fit. We can spin up a server to house the mart in the cloud in a couple of hours. Let us help you. Use the tools you want… we think that it is cool that you are going to try out some new stuff… but if you want to use the tools we provide then you’ll get the benefit of our licensing deal and the benefit of our support… but you decide. We need IT to allow a little chaos…

This, I believe is what cloud offers to the data warehouse space…. the platform to respond.

But there is a rub… data warehouse appliances from Teradata, Exadata, and Netezza require bundled hardware that is not going to fit in your cloud. A shared-nothing architecture is a tough fit into the shared disk paradigm of the cloud (see here). The I/O reliance of a disk-based DBMS make performance tough on a shared disk platform. I think that for data marts and analytic sandboxes the cloud is the right choice… if you want to minimize the size of the shadow IT cast by lines of business. An in-memory database (IMDB): HANA, TimesTen, or SQLFire may be the best alternative for a small cloud-based mart.

David Linthicum has it right in spades for the data warehouse space… we need some user pull-through… and we need cloud computing as the platform to make these user-driven initiatives manageable.


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