Teradata’s Excellent Memory Management… and Poor Memory Utilization

I was recently surprised to hear from a prospect that Teradata’s memory management was considered a differentiator over Greenplum. This is not because of bad information… Teradata probably does have better memory management… but they have better memory management because they use memory less efficiently than Greenplum. Let me explain…

First let’s be clear… the utilization of memory is not measured by the amount of memory required… but by the amount required times the amount of time it is required. Think about it… if you have a query that requires 16MB of memory and holds it for 1 second… and another query that requires 4MB of memory and holds it for 4 seconds… the effective memory utilization is the same.

Greenplum uses pipelining to flow data from step to step in the query plan. Teradata writes the results from each step in the query plan to disk, to a spool file. This architectural difference allows Greenplum to complete any single query in a small fraction of the time that Teradata requires… as Teradata pays the cost of writing results after each step and of reading these results into the subsequent steps add up. The result is that Teradata uses a smaller memory footprint for each query… but holds the memory significantly longer… resulting in relatively poor memory utilization.

Note that this is not really bad design by Teradata… it is just old design. Once upon a time the servers Teradata ran on had only a little memory… as little as 32MB… so they had to spool data to disk to make it all fit. Greenplum is designed for modern processors with 100X more memory… and we use that memory effectively to get queries in and out as fast as possible.

By the way, as a side effect Greenplum does not require management of spool space… so this sysadmin task is eliminated.

So… Teradata does tightly manage memory… but this is not an advantage… they manage it tightly because they have to.

The Worst Data Warehouse in the World

So far this blog has focused on issues related to database architecture… so this title might not seem on message. But architecture has implications.

The aim of any BI system is to support the decision-making process of the business. BI infrastructure is clearly a success when your company learns to make fact-based decisions as part of the day-to-day operation of the business. The best data warehouse in the world would be one that provides such effective decision support that the business gains a competitive advantage over the competition.

But I often run into companies where sweet success has turned sour. Why, because in these sour situations the BI eco-system cannot keep up. In these bad cases the best data warehouse in the world becomes the worst.

Usually the problem comes in one of two flavors: either the required decision support is unavailable in time to make a decision, or the eco-system cannot extend to support new business opportunities.

The first case usually shows up during periods when decision-making increases: during seasonal peaks in business. The second appears when the business grows: after a merger or when a new product is introduced. In both cases the cost of the failure is significant.

But these worst cases do not happen out of the blue. They creep up on you. There are symptoms. Often the first symptom is when the nightly reporting process starts missing its service level targets. That is, the nightly load of the warehouse and the refresh of the indexes, materialized views, the summary tables, the cubes, and the marts; and then the running of reports cannot complete in the batch window. This is followed by slow response in your online query processing as the nightly process creeps into the day. Then, the business asks for more users and/or for more data to be added and the problem grows… until decision-making is delayed or unsupported altogether.

Sadly, this problem is avoidable and the solution is well understood. All that is required is a scalable foundation that can extend through the addition of relatively inexpensive hardware. If you could easily add storage and compute then as the constraints hit you can scale up.

A shared nothing architecture scales. We have examples at Greenplum of production systems that scale from hundreds of gigabytes to thousands of terabytes… and other shared nothing vendors: Teradata and Netezza at least, can boast the same. When our customers run out of gas we add hardware. And the architecture scales bigger still… shared nothing is the foundation for all web scale data base technology… scaling to hundreds of petabytes.

So why do companies build, and continue to build, on shared memory systems with built-in limits? Because… they continually underestimate the growth in data… the failure is a failure of vision (consider the name “Teradata”… selected when a terabyte was considered nearly unreachable). Data does not just grow, it explodes in leaps and bounds as technology advances.

But let’s be real… Why do companies really select limiting infrastructure? Because they mistakenly believe that they can build BI infrastructure on technology designed for OLTP… and they already have DBAs trained on this technology who heavily influence the decision. Or, they have an enterprise license for the OLTP database and they want to save some money.

I imagine that I’ve made my point. The worst data warehouse in the world is a warehouse that constrains your business… one that cannot scale as the demand for data and decision support grows… one that costs you hundreds of thousands of dollars in staff time with every change… one that is tuned to the breaking point, rather than robust.

Why would anyone ever put their business at risk like this?

Greenplum and Teradata: Simliar Architecture, Different Strategies

Hardware systems, servers and network fabric, provide the foundation upon which all shared-nothing database management systems rest. Hardware systems are a major contributor to the overall price/performance and total cost of ownership for a data warehouse platform. This blog considers the hardware strategies of EMC/Greenplum: applying the idea of using common, off-the-shelf (COTS) components to build a competitive foundation; and Teradata: developing a proprietary hardware system by tightly integrating components.

Strategies

The Teradata hardware strategy is simple to describe. They expend R&D dollars to couple low-level technologies into a tightly integrated system. Their servers are custom-designed within a set of guidelines that allows both the LINUX and Microsoft Windows operating systems to execute there. Their network fabric is highly proprietary, using cycles within the fabric to offload sort/merge data processing from the server CPU.

In other words, Teradata believes that the time and effort required to engineer an integrated proprietary offering will improve the performance of their offering enough to offset the cost.

EMC and Greenplum have taken a different approach. They have elected a strategy that leverages off-the-shelf servers offered by hardware vendors like Dell or HP, and network switches from vendors like Brocade, Arista, and Cisco. They have elected to expend few dollars on hardware design and development and to leverage the R&D investments made by these other vendors. In other words, Greenplum believes that the advantages in price and performance provided by using off-the-shelf hardware provides a sustainable advantage.

Price

The lower costs associated with Greenplum’s strategy clearly provide an advantage. Greenplum does not have to expend to design and manufacture custom hardware. The manufacturing costs may not be significant, but the staff costs required by the Teradata strategy must affect the price. Clearly the Greenplum strategy provides an advantage on the price side.

Performance

The Teradata strategy has to be about performance… so lets speculate:

  • How much of a performance increase might their integration provide on the server-side?
  • How much of a performance increase might their integration provide on the network side?

In the days before there was a microprocessor based enterprise server market, Teradata could gain substantially here. Microprocessors were built for personal computing and not designed for the high-availability and high-performance requirements in an enterprise. Teradata had much to gain from building rather than buying server.

But today, there is little to gain from a highly customized design. The requirement to run standard LINUX and Windows operating systems limit their ability to innovate and the resulting servers have to be very similar to those built for off-the-shelf enterprise servers. There is little or no performance advantage here.

On the network side, there once was a distinct advantage to Teradata’s ByNet. It was both faster than available off-the shelf switches and it offloaded cycles from the under-powered CPU. Today, however, there are plenty of cheap, fast switches… so the speed advantage has disappeared. Worse still, the introduction of multi-core CPUs have eliminated the advantage of the in-the-switch sort/merge that makes ByNet unique. CPU is inexpensive these days.

The bottom line: it is unclear if the Teradata hardware strategy affords them a performance advantage.

Cost of Ownership

An argument could be made that supporting COTS hardware is inherently less expensive than supporting a Teradata cluster. But there is a more substantial savings that is clear.

Every 2-3 years, as newer Teradata technology obsoletes your currently installed cluster the value of the current hardware goes to zero and the cost of ownership goes up significantly. The costs of this are especially high when you are required to add several nodes to accommodate growth as Teradata refreshes their technology. You may have to buy servers that are already obsolete.

With Greenplum, your current cluster is built from general-purpose servers that are re-purposed with ease. In fact, since the nodes in a Greenplum cluster are usually high-end servers, customers often cycle new technology into their data warehouse and cycle the old servers out into their server farm. The result is a higher performance warehouse and full use of all of the server technology.

A Final Word

The words “proprietary hardware” are sometimes thrown around as an insult. But Teradata’s proprietary approach is based on the belief that a tightly integrated configuration adds benefit to offset the costs. Greenplum believes that today the enterprise server and the network switch vendors have matured their products to the point where off-the-shelf technology can match or exceed the performance of custom hardware… at a significantly reduced cost. You may have an opinion or you may wait to see how the benchmarks, the proof-of-concepts, and the market decide… but its interesting to understand the differing approaches.

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