How DBMS Vendors Admit to an Architectural Limitation: Part 2 – Teradata Intelligent Memory

This is the second post (see Part 1 here) on how vendors adjust their architecture without admitting that the previous architecture was flawed. This time we’ll consider Teradata and in-memory….

When SAP HANA appeared Teradata went on the warpath with a series of posts and statements that were pointed but oddly miscued (see the references below). According to the posts in-memory was unnecessary and SAP was on a misguided journey.

Then Teradata announced Intelligent Memory and in-memory was cool. This is pretty close to an admission that SAP was right and Teradata was wrong. The numbers which drove Teradata here are compelling… 100K-200K ns to access an SSD device or 100 ns to access DRAM… a 1000X reduction… and the latency to disk is 100X worse than SSD.

Intelligent Memory was announced shortly after the release of Teradata’s columnar table type. Column-orientation is important because you need a powerful approach to compression to effectively use an expensive memory resource… and columnar provides this. But Teradata, like Greenplum, extended a row-based engine to support columns in order to get to market quick… they hoped to get 80% of the effectiveness of in-memory with only 20% of the engineering effort. The other 20% comes when you develop a new engine that fully exploits the advantages of a columnar architecture. These advanced exploits allow HANA, DB2 BLU, and Oracle 12c to execute directly on columnar data thereby avoiding decompression, fully utilizing the processor caches, and allowing sets to be operated on by super-computing vector-processing instructions. In fact, Teradata really applied the 50/20 rule… they gained 50%, maybe only 40%, of the benefits with their columnar and Intelligent Memory features… but it was easy to deploy what is in-effect an in-memory cache over their existing relational engine.

Please don’t jump to the wrong conclusion here… Intelligent Memory is a strong product. If you were to put hot data in memory, cool data in Teradata-on-SSD-or-Disk, and cold data in Hadoop and manage them as one EDW you could deploy a very cost-effective platform (see here).

Still, Teradata with Intelligent Memory is not likely to compete effectively against HANA, BLU, or 12c for raw performance… so there will be some marketing foam attached and an appeal for Teradata shops to avoid database apostasy and stick with them. You can see some of the foam in the articles below.

A quick aside here… generally a DBMS should win or lose based on price/performance. The ANSI standard makes a products features nearly, not completely but nearly, irrelevant. If you cannot win on price/performance then you blow foam. When any vendor starts talking about things like TCO you should grab your wallets… it is an appeal to foaminess to hide a weakness. I’m not calling out Teradata here… this is general warning that applies to every software vendor.

Intelligent Memory is a smart move. While it may not win in a head-to-head POC… it will be close-ish… close enough to keep the congregation in their pews. As readers know, I am not a big fan of technical religiosity… being a “Teradata-shop” is lazy… engineers we should pick the best solution and learn it. The tiered approach mentioned three paragraphs up is a good solution and non-Teradata shops should be considering it… but Teradata shops should be open to new technology as well. Still, we should pick new technology with a sensitivity to the cost of a migration… and in many cases Intelligent Memory will save business for Teradata by getting just close enough to make migration a bad trade-off. This is why it was so smart.

Back to the theme of these posts… Teradata back-tracked on the value of in-memory… and in the process admitted-without-admitting a shortcoming in their architecture. So it goes…

Next we will consider whether you should be building data warehouses on z/OS using DB2 or the DB2 Analytics Accelerator aka Netezza.

References

Database Fog Blog

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