Truviso and EnterpriseDB blend event processing with ordinary database management
Truviso and EnterpriseDB announced today that there’s a Truviso “blade” for Postgres Plus. By email, EnterpriseDB Bob Zurek endorsed my tentative summary of what this means technically, namely:
There’s data being managed transactionally by EnterpriseDB.
Truviso’s DML has all along included ways to talk to a persistent Postgres data store.
If, in addition, one wants to do stream processing things on the same data, that’s now possible, using Truviso’s usual DML.
The Mark Logic story in XML database management
Mark Logic* has an interesting, complex story. They sell a technology stack based on an XML DBMS with text search designed in from the get go. They usually want to be known as a “content” technology provider rather than a DBMS vendor, but not quite always.
*Note: Product name = MarkLogic, company name = Mark Logic.
I’ve agreed to do a white paper and webcast for Mark Logic (sponsored, of course). But before I start serious work on those, I want to blog based on what I know. As always, feedback is warmly encouraged.
Some of the big differences between MarkLogic and other DBMS are:
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MarkLogic’s primary DML/DDL (Data Manipulation/Description Language) is XQuery. Indeed, Mark Logic is in many ways the chief standard-bearer for pure XQuery, as opposed to SQL/XQuery hybrids.
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MarkLogic’s XML processing is much faster than many alternatives. A client told me last year that – in an application that had nothing to do with MarkLogic’s traditional strength of text search – MarkLogic’s performance beat IBM DB2/Viper’s by “an order of magnitude.” And I think they were using the phrase correctly (i.e., 10X or so).
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MarkLogic indexes all kinds of entities and facts, automagically, without any schema-prebuilding. (Nor, I gather, do they depend on individual documents carrying proper DTDs.) So there actually isn’t a lot of DDL. (Mark Logic claims in one test MarkLogic had more or less 0 DDL, vs. 20,000 lines in DB2/Viper.) What MarkLogic indexes includes, as Mark Logic puts it:
- Every word
- Every piece of structure
- Every parent-child relationship
- Every value.
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As opposed to most extended-relational DBMS, MarkLogic indexes all kinds of information in a single, tightly integrated index. Mark Logic claims this is part of the reason for MarkLogic’s good performance, and asserts that competitors’ lack of full integration often causes overhead and/or gets in the way of optimal query plans. (For example, Mark Logic claims that Microsoft SQL Server’s optimizer is so FUBARed that it always does the text part of a search first.) Interestingly, Intersystems’ object-oriented Cache’ does pretty much the same thing.
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MarkLogic is proud of its text search extensions to XQuery. I’ve neglected to ask how that relates to the XQuery standards process. (For example, text search wasn’t integrated into the SQL standard until SQL3.)
Other architectural highlights include: Read more
| Categories: Data types, IBM and DB2, MarkLogic, Structured documents | 3 Comments |
ParAccel pricing
I made a round of queries about data warehouse software or appliance pricing, and am posting the results as I get them. Earlier installments featured Teradata and Netezza. Now ParAccel is up.
ParAccel’s software license fees are actually very simple — $50K per server or $100K per terabyte, whichever is less. (If you’re wondering how the per-TB fee can ever be the smaller one, please recall that ParAccel offers a memory-centric approach to sub-TB databases.)
Details about how much data fits on a node are hard to come by, as is clarity about maintenance costs. Even so, pricing turns out to be one of the rare subjects on which ParAccel is more forthcoming than most competitors.
| Categories: Analytic technologies, Data warehousing, ParAccel, Pricing | 3 Comments |
Yet another data warehouse database and appliance overview
For a recent project, it seemed best to recapitulate my thoughts on the overall data warehouse specialty DBMS and appliance marketplace. While what resulted is highly redundant with what I’ve posted in this blog before, I’m sharing anyway, in case somebody finds this integrated presentation more useful. The original is excerpted to remove confidential parts.
… This is a crowded market, with a lot of subsegments, and blurry, shifting borders among the subsegments.
… Everybody starts out selling consumer marketing and telecom call-detail-record apps. …
Oracle and similar products are optimized for updates above everything else. That is, short rows of data are banged into tables. The main indexing scheme is the “b-tree,” which is optimized for finding specific rows of data as needed, and also for being updated quickly in lockstep with updates to the data itself.
By way of contrast, an analytic DBMS is optimized for some or all of:
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Small numbers of bulk updates, not large numbers of single-row updates.
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Queries that may involve examining or returning lots of data, rather than finding single records on a pinpoint basis.
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Doing arithmetic calculations – commonly simple arithmetic, sorts, etc. – on the data.
Database and/or DBMS design techniques that have been applied to analytic uses include: Read more
Optimizing WordPress database usage
There’s an amazingly long comment thread on Coding Horror about WordPress optimization. Key points and debates include:
- WordPress makes scads of database calls on every page. (20 is the supposed default number. That sounds a little high to me, but not wholly incredible.)
- Therefore one should use a caching plug-in. WP-Cache is the preferred one. WP-Super-Cache gets some votes as perhaps being even better.
- In theory the database cache should handle most of the problem. (After all, many of those database queries are the same for every page.) In practice, it often doesn’t, even if you use dedicated (as opposed to shared) web hosting.
- LAMP vs. Microsoft stack (uh-oh).
- Drupal vs. WordPress vs. Movable Type vs. Joomla vs. do-it-yourself (uh-oh too).
Another theme is — well, it’s WordPress “theme” design. Do you really need all those calls? The most dramatic example I can think of one I experienced soon after I started this blog. Some themes have the cool feature that, in the category list on the sidebar, there’s a count of the number of posts in the category. Each category. I love that feature, but its performance consequences are not pretty.
As previously noted, we’ll be doing an emergency site upgrade ASAP. Once we’re upgraded to WordPress 2.5, I hope to deploy a rich set of back-end plug-ins. One of the caching ones will be among them.
| Categories: About this blog, Application areas, Cache | 1 Comment |
DATAllegro finally has a blog
It took a lot of patient nagging, but DATAllegro finally has a blog. Based on the first post, I predict:
- DATAllegro’s blog will live up to CEO Stuart Frost’s talent for clear, interesting writing.
- Like a number of other vendor blogs — e.g., Netezza’s — DATAllegro’s will have infrequent but usually long posts.
The crunchiest part of the first post is probably
Another very important aspect of performance is ensuring sequential reads under a complex workload. Traditional databases do not do a good job in this area – even though some of the management tools might tell you that they are! What we typically see is that the combination of RAID arrays and intervening storage infrastructure conspires to break even large reads by the database into very small reads against each disk. The end result is that most large DW installations have very large arrays of expensive, high-speed disks behind them – and still suffer from poor performance.
I’ve pounded the table about sequential reads multiple times — including in a (DATAllegro-sponsored) white paper — but the point about misleading management tools is new to me.
Now if I could just get a production DATAllegro reference, I’d be completely happy …
| Categories: Analytic technologies, DATAllegro, Data warehouse appliances, Data warehousing | Leave a Comment |
Netezza pricing
In connection with the announcement of the Teradata 2500, I asked some Teradata competitors about pricing. Netezza’s response amounted to “We don’t disclose list pricing, but our cheapest system handles about 3 1/4 TB and sells for under $200K.” So Netezza’s actual pricing is well below the list price of the Teradata 2500.
| Categories: Data warehouse appliances, Data warehousing, Netezza, Pricing, Teradata | 11 Comments |
Teradata introduces lower-cost appliances
After months of leaks, Teradata has unveiled its new lines of data warehouse appliances, raising the total number either from 1 to 3 (my view) or 0 to 2 (what you believe if you think Teradata wasn’t previously an appliance vendor). Most significant is the new Teradata 2500 series, meant to compete directly with the smaller data warehouse specialists. Highlights include:
- An oddly precise estimated capacity of “6.12 terabytes”/node (user data). This estimate is based on 30% compression, which is low by industry standards, and surely explains part of the price umbrella the Teradata 2500 is offering other vendors.
- $125K/TB of user data. Obviously, list pricing and actual pricing aren’t the same thing, and many vendors don’t even bother to disclose official price lists. But the Teradata 2500 seems more expensive than most smaller-vendor alternatives.
- Scalability up to 24 nodes (>140 TB).
- Full Teradata application-facing functionality. Some of Teradata’s rivals are still working on getting all of their certifications with tier-1 and tier-2 business intelligence tools. Teradata has a rich application ecosystem.
- What will be controversial performance, until customer-benchmark trends clearly emerge.
| Categories: Analytic technologies, Data warehouse appliances, Data warehousing, Database compression, Pricing, Teradata | 5 Comments |
Kickfire kicks off
I chatted with Raj Cherabuddi and others on the Kickfire (formerly C2) team for over an hour on Monday, and now have a better sense of their story. There are some very basic questions I still don’t have answers to; I’ll fill those in when I can.
Highlights of what I have and haven’t figured out so far include:
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Kickfire’s technology has two main parts: A SQL co-processor chip and a MySQL storage engine.
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Kickfire makes a Type 0 appliance. If I understood correctly, it contains the chip, a couple of standard CPU cores, and 64 gigs of RAM. Or else it contains just the chip, and is meant to be hooked up to a 2U box with 64 gigs of RAM. I’m confused.
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The Kickfire box can handle up to 3 terabytes of user data. The disk required for that is 4-5 terabytes without redundancy, 2X with. Based on that formulation and other clues, I’m guessing Kickfire — unlike other appliance vendors — doesn’t build in storage itself.
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I don’t know whether the Kickfire chip is true custom silicon or an FPGA emulation.
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The essential idea of the chip is dataflow programming for SQL, with pipelining between operations. This eliminates the overhead of registers and context switching. I don’t know what the trade-offs are, if any.
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Kickfire’s database software is columnar, operating on compressed data even in RAM. In that, Kickfire’s story is most similar to Vertica’s, although I’m guessing Exasol may do something similar as well. Like Vertica, Kickfire uses multiple compression methods (they’re reluctant to give detail, but agreed it would be fair to say they use both something like dictionary/token and something like delta compression).
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Kickfire’s software is ACID-compliant. You can do incremental loads or trickle feeds. Bulk load speed is 100 Gb/hour. Kickfire’s solution for the traditional problem of updating column stores is called “snapshots.” Without giving details, they position that as similar to the Vertica solution.
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Like other MySQL storage engines, Kickfire inherits whatever data connectivity, stored procedure capabilities, user-defined functions ability, etc. that MySQL has.
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Kickfire has no paying customers, but does have a slide showing many logos of “prospects and beta customers.”
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Kickfire has no MPP capabilities at this time, but says adding those is “on the roadmap” and will be “easy.”
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Kickfire submitted a 100 Gb TPC-H result, in which it beat the previous leaders — Exasol, ParAccel, and Microsoft – on price-performance, and lagged only Exasol and ParAccel on absolute performance. Kickfire is extremely proud of this. Indeed, I don’t recall another vendor ascribing that much weight to them in the entire history of TPCs.* Kickfire seems unfazed by the fact that its result is for a system listed with a ship date 6 months in the future (I’m guessing that’s the latest the TPC will allow), while the other results are for systems available today.
*Somebody – perhaps adman extraordinaire Rick Bennett? — may want to check my memory on this, but I think Oracle’s famed “Gentlemen, start your snails” ad in the early 1990s was about PC World tests, not TPCs. Oracle also had an ad about WW1-style planes nosediving, but I don’t think those referenced TPCs either.
Relational purists should root for ScaleDB
I just put up a long post about a small development-stage company, ScaleDB. The punchline is that ScaleDB has a data access method — an extension of Patricia tries — that gives referential integrity and updatable views for free.
People who think current “relational” DBMS aren’t relational enough often suggest that’s the kind of foundation DBMS should have. And unlike Required Technologies’ TransRelational (TM) shtick, ScaleDB’s really is an OLTP-oriented approach.
| Categories: MySQL, Theory and architecture, TransRelational | Leave a Comment |